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FILE: README-fr-FR.md
================================================
# Parcours d'Apprentissage de A à Z: Le Machine Learning pour Ingénieur Logiciel
<p align="center">
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers">
<img alt="Top-down learning path: Machine Learning for Software Engineers" src="https://img.shields.io/badge/Machine%20Learning-Software%20Engineers-blue.svg">
</a>
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/ZuzooVn/machine-learning-for-software-engineers.svg">
</a>
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers/network">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/ZuzooVn/machine-learning-for-software-engineers.svg">
</a>
</p>
Inspiré par [Coding Interview University](https://github.com/jwasham/coding-interview-university/blob/master/translations/README-fr.md).
Traductions: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-pt-BR.md) | [中文版本](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-zh-CN.md) | [Français](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-fr-FR.md)
[Comment moi (Nam Vu) prévoit de devenir un ingénieur dans le domaine du Machine Learning ](https://www.codementor.io/zuzoovn/how-i-plan-to-become-a-machine-learning-engineer-a4metbcuk)
## Qu'est-ce que c'est?
Il s'agit d'un projet qui s'étend sur plusieurs mois, avec pour objectif de devenir ingénieur en Machine Learning en partant de mon expérience de développeur mobile (de manière autodidacte, sans diplôme en Informatique).
Mon objectif principal est d'élaborer une méthode pour apprendre le Machine Learning qui se base surtout sur la pratique et écarte au mieux l'aspect Mathématique pouvant rebuter les novices.
C'est une approche non conventionnel car de type analyse-synthèse fondée d'abord à partir de résultats, elle est conçue du point de vue ingénierie logiciel.
Toute contributions pouvant améliorer le projet est la bienvenue.
---
## Table des Matières
- [Qu'est-ce que c'est?](#quest-ce-que-cest)
- [Pourquoi l'utiliser?](#pourquoi-lutiliser)
- [Comment s'en servir?](#comment-sen-servir)
- [Suivez-moi](#suivez-moi)
- [Ne vous laissez pas abattre](#ne-vous-laissez-pas-abattre)
- [Au sujet de nos vidéos](#au-sujet-de-nos-vidéos)
- [Les Connaissances Requises](#les-connaissances-requises)
- [Le Planning Journalier](#le-planning-journalier)
- [Motivation](#motivation)
- [Découvrir le Machine learning](#découvrir-le-machine-learning)
- [Maîtriser le Machine learning](#maîtriser-le-machine-learning)
- [Le Machine learning c'est fun](#le-machine-learning-cest-fun)
- [Inky Machine Learning](#inky-machine-learning)
- [Machine Learning: Un Guide de A à Z](#machine-learning-un-guide-de-a-à-z)
- [Parcours et expériences](#parcours-et-expériences)
- [Algorithmes de Machine Learning](#algorithmes-de-machine-learning)
- [Ouvrages pour débutants](#ouvrages-pour-débutants)
- [Livres pratique](#livres-pratique)
- [Les Compétitions Kaggle](#les-compétitions-kaggle)
- [Séries vidéos](#séries-vidéos)
- [MOOC](#mooc)
- [Ressources](#ressources)
- [Participer aux projets Open-source](#participer-aux-projets-open-source)
- [Jeux](#jeux)
- [Podcasts](#podcasts)
- [Communautés](#communautés)
- [Conférences](#conférences)
- [Questions aux Entretiens](#questions-aux-entretiens)
- [Les Entreprises que j'admire](#les-entreprises-que-jadmire)
---
## Pourquoi l'utiliser?
Je suis ce programme pour me préparer à mon futur métier, celui d'ingénieur en Machine learning. Je développe des applications mobile sur les plateformes Android, iOS et Blackberry depuis 2011. J'ai un diplôme en Ingénierie Logiciel mais pas en Informatique. J'ai également quelques bases en calcul, algèbre linéaire, Mathématiques discrètes, probabilités et statistiques grâce à mon parcours à l'Université.
En pensant à mon intérêt pour le Machine learning :
- [Est-ce que je peux apprendre et engager une carrière professionnelle dans le Machine learning sans un Master ou un Doctorat en Informatique ?](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD)
- *"C'est possible, mais ce sera bien plus difficile que lorsque je me suis orienter vers ce domaine."* [Drac Smith](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD/answer/Drac-Smith?srid=oT0p)
- [Comment puis-je obtenir un travail dans ce domaine en tant que développeur logiciel qui a appris le Machine learning en autonomie mais n'a jamais eu l'occasion de l'utiliser en milieu professionnel ?](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work)
- *"Je ne recrute que des experts en Machine learning pour mon équipe et votre MOOC ne vous permettra pas d'obtenir ce travail (il y a quand même de bonnes nouvelles plus bas). En faite, beaucoup de gens avec un master en Machine learning n'auront pas nécessairement ce travail car ils (et la plupart des gens qui ont suivie une MOOC) n'ont pas une compréhension suffisamment précise pour répondre à mes problématiques."* [Ross C. Taylor](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work/answer/Ross-C-Taylor?srid=oT0p)
- [Quelles sont les compétences requises pour travailler dans le domaine du Machine learning ?](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs)
- *"D'abord, vous devez avoir une expérience correcte dans l'Informatique/les Mathématiques. Machine learning (ML) est un sujet complexe et la plupart des ouvrages attendent de vous que vous ayez déjà un lourd bagage technique. Ensuite ML est un vaste sujet qui se décline en beaucoup de sous-domaines dont chacune demande des compétences précises. Vous devriez vous renseigner sur le contenu d'un programme MS en Machine learning pour voir le cours, le programme et les manuels scolaires."* [Uri](http://softwareengineering.stackexchange.com/a/79717)
- *"Probabilités, Calculs distribués et Statistiques."* [Hydrangea](http://softwareengineering.stackexchange.com/a/79575)
Je me retrouve alors dans une période de doute.
AFAIK, [On distingue deux aspect dans le Machine learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/):
- Machine Learning en pratique: Il s'agit de faire des requêtes sur des bases de données, du nettoyage, écrire des scripts pour transformer les données, faire correspondre les programmes avec les bibliothèques adéquates et écrire du code sur mesure pour obtenir des réponses fiables capable de satisfaire des questions encore trop flou. On doit s'adapter à une réalité imparfaite, pleine d'aléas, où tout est en désordre.
- Machine Learning en théorie: Ici on est en plein dans les Mathématiques et l'abstraction, on considère les cas idéaux sans se soucier de ce qui est réellement possible. Tout est bien plus ordonné et propre car on néglige les contraintes du réel.
Je pense que la meilleure approche pour une méthodologie orientée sur la pratique ressemble à quelque chose comme
['pratique — apprentissage — pratique'](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985), ça signifie qu'on étudie d'abord des projets existants avec leurs problèmes et solutions (pratique) pour se familiariser avec les méthodes traditionnelles dans une discipline et peut-être également se familiariser avec une méthodologie propre à ce projet en particulier.
Après avoir manipuler des cas élémentaires, il est temps d'ouvrir les livres et autres manuels pour étudier ce qui se passe réellement au niveau théorique, ce qui va ensuite donner plus de sens aux prochains essais dans la pratique. En effet, ce qu'on apprend via la théorie s'ajoutera à une sorte de *Boîte à Outils* englobant les connaissances pour résoudre tel ou tel problème pratique. Etudier la théorie permet aussi d'améliorer la compréhension des expériences basiques pour se construire des bases solides et donnera lieu à un apprentissage plus rapide des expériences d'un niveau plus pointu.
> *“Il faut d'abord apprendre la façon de faire, ensuite on peut faire à
> sa façon.”*
Pour moi c'est un plan sur le long terme qui me prendra certainement des années. Si vous êtes déjà familier avec tout ceci, vous aurez probablement besoin de moins de temps.
## Comment s'en servir?
Tout ce qui suis est un guide qui vous apprendra de manière progressive le Machine learning via les points qui sont listés plus bas, je vous conseille de consulter chaque points, dans l'ordre, de haut en bas.
Je profite au mieux de ce qu'offre la version GitHub de Markdown, pour inclure notamment un système de tâches à réaliser. J'utilise des cases à cocher, ce qui vous permet de garder un œil sur votre progression.
- [x] Créez une nouvelle branche pour pouvoir cocher des cases comme celle-ci. Il suffit d'ajouter un x entre les crochets [x].
[Plus à propos de Markdown à la sauce GitHub](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Suivez-moi
Je suis un Ingénieur Logiciel vietnamien, un véritable passionné et mon rêve est de pouvoir un jour travailler aux Etats-Unis.
Combien de temps par jour ai-je travailler pendant ce programme? À peu près 4 heures/nuit après une bonne journée de boulot.
Et l'aventure continue.
- Twitter: [@Nam Vu](https://twitter.com/zuzoovn)
| |
|:---:|
| USA as heck |
## Ne vous laissez pas abattre
J'ai été décourager plusieurs fois devant les cours et ouvrages qui me signalaient d'entré de jeu que la maîtrise de concept tels que les fonctions de plusieurs variables, l'inférence statistique et l'algèbre linéaire sont obligatoires pour comprendre le Machine Learning. Et je ne sais toujours pas par où commencer...
Même si tout ces termes peuvent rebuter il ne faut pas se démotiver pour autant, voici quelques pistes pour démarrer votre apprentissage :
- [Et si je ne suis pas bon en Mathématique ?](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [5 Techniques Pour Comprendre les Algorithme de Machine Learning Sans Expériences en Mathématiques](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/)
- [Comment apprendre le Machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
> *Ne jamais baisser les bras! L'échec et le rejet ne sont que les premières étapes du succès.* - **Jim Valvano**
## Au sujet de nos vidéos
Certaines vidéos ne sont disponible qu'en s'inscrivant aux cours de Coursera ou EdX. Ces outils sont gratuit mais il arrive que les cours soient indisponible et vous devez patienter quelques mois pour y avoir accès.
Pour limiter ce problème, je vais ajouter davantage de vidéos provenant de sources publiques pour remplacer au fil du temps les cours en ligne en prenant par exemple des cours magistraux d'Universités.
## Les Connaissances Requises
Je vais lister dans cette courte section les aptitudes et informations intéressantes que je voulais apprendre avant de commencer mon programme quotidien en Machine learning.
- [ ] [Quelle sont les différences entre Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, et Big Data?](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1)
- [ ] [Apprendre comment apprendre](https://www.coursera.org/learn/learning-how-to-learn)
- [ ] [Ne brisez pas la chaîne](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [ ] [Comment apprendre par vous même](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## Le Planning Journalier
Chaque matière ne demandent pas une journée entière pour être assimilée, vous pouvez en faire plusieurs par jour.
Chaque jours, je choisis un sujet de la liste juste en dessous et je le lis entièrement, je prends des notes et fais les exercices. Pour finir, j'écris une première implémentation en Python ou en R.
# Motivation
- [ ] [Dream](https://www.youtube.com/watch?v=iwnIrKyXtSw)
## Découvrir le Machine learning
- [ ] [Introduction au Machine Learning en image](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [Un guide pour aborder le Machine Learning en douceur](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)
- [ ] [Introduction au Machine Learning pour développeur](http://blog.algorithmia.com/introduction-machine-learning-developers/)
- [ ] [Les bases du Machines Learning pour débutant](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
- [ ] [Comment expliquer le Machine Learning et le Data Mining aux non-initiés?](https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people)
- [ ] [Machine Learning: sous le capot. Blog post explique les principes du Machine Learning à monsieur Tout-le-monde. Simple et clair.](https://georgemdallas.wordpress.com/2013/06/11/big-data-data-mining-and-machine-learning-under-the-hood/)
- [ ] [Qu'est-ce que le Machine Learning et comment est-ce que ça fonctionne?](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1)
- [ ] [Deep Learning - Une introduction toute simple](http://www.slideshare.net/AlfredPong1/deep-learning-a-nontechnical-introduction-69385936)
## Maîtriser le Machine learning
- [ ] [Les méthodes pour maîtriser le Machine Learning](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [Le Machine Learning pour les programmeurs](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [Machine Learning appliquée avec Machine Learning Mastery](http://machinelearningmastery.com/start-here/)
- [ ] [Mini-cours de Python pour le Machine Learning](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [Mini-cours sur les Algorithmes de Machine Learning](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## Le Machine learning c'est fun
- [ ] [Machine Learning c'est fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Partie 2: Utiliser le Machine Learning pour generer des niveaux de Super Mario Maker](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [Partie 3: Deep Learning et Convolutional Neural Networks](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637)
- [ ] [Partie 4: Reconnaissance faciale moderne grâce au Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc)
- [ ] [Partie 5: La traduction de langue grâce au Deep Learning et la magie des suites.](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c)
- [ ] [Partie 6: Comment faire de la reconnaissance vocale avec le Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a#.lhr1nnpcy)
- [ ] [Partie 7: Exploiter les Réseaux antagonistes génératifs pour faire du Pixel Art 8-bit](https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7)
- [ ] [Partie 8: Comment tromper volontairement les réseaux de neurones](https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196)
## [Inky Machine Learning](https://triskell.github.io/2016/11/15/Inky-Machine-Learning.html)
- [ ] [Partie 1: Qu'est-ce que le Machine Learning ?](https://triskell.github.io/2016/10/23/What-is-Machine-Learning.html)
- [ ] [Partie 2: L'apprentissage supervisée et non-supervisée](https://triskell.github.io/2016/11/13/Supervised-Learning-and-Unsupervised-Learning.html)
## Machine Learning: Un Guide de A à Z
- [ ] [ Aperçu, objectifs, type d'apprentissage et algorithmes](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [Sélection de Données, préparation et modélisation](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [Evaluation de model, validation, complexité et amélioration](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [Performance de model et analyse d'erreur](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [Apprentissage non-supervisée, matières similaires et Machine Learning dans la pratique](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## Parcours et expériences
- [ ] [Machine Learning en une semaine](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [Machine Learning en une année](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [Comment j'ai écris mon premier programme de Machine Learning en 3 jours](http://blog.adnansiddiqi.me/how-i-wrote-my-first-machine-learning-program-in-3-days/)
- [ ] [Learning Path : Votre mentor pour devenir un expert en Machine Learning](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [Vous aussi pouvez devenir une grande figure du Machine Learning! Sans Doctorat](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7)
- [ ] Comment devenir un Data Scientist en 6 mois: Une approche non-conventionnelle pour organiser votre carrière.
- [Vidéo](https://www.youtube.com/watch?v=rIofV14c0tc)
- [Diapositive](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [5 compétences nécessaire pour devenir Ingénieur en Machine Learning](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html)
- [ ] [Êtes-vous un Ingénieur en Machine Learning autodidacte ? Si oui, comment avez-vous fait et combien de temps est-ce que cela vous a pris ?](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you)
- [ ] [Comment devenir un bon Ingénieur en Machine Learning ?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
- [ ] [Un apprentissage sabbatique centré sur le Machine Learning](http://karlrosaen.com/ml/)
## Algorithmes de Machine Learning
- [ ] [10 Algorithme de Machine Learning expliqués à un ‘Soldat’](https://www.analyticsvidhya.com/blog/2015/12/10-machine-learning-algorithms-explained-army-soldier/)
- [ ] [Top 10 des algorithmes de Data Mining en pur Anglais](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/)
- [ ] [10 Termes de Machine Learning expliqués en Anglais basique](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/)
- [ ] [Un tour d'horizon des algorithmes de Machine Learning](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
- [ ] [Les 10 algorithmes de Machine Learning que les Ingénieurs doivent absolument connaître](https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.ofc7t2965)
- [ ] [Comparer les algorithmes d'apprentissages supervisées](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
- [ ] [Algorithmes de Machine Learning: Une collection d'implémentations minimales et claires](https://github.com/rushter/MLAlgorithms)
## Ouvrages pour débutants
- [ ] [Data Smart: Utiliser la Science des Données pour transformer l'information en connaissance 1ère Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [La Science des données pour le Commerce: Ce que vous devez savoir sur le Data Mining et la pensée analytique des données](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)
- [ ] [Predictive Analytics: Le pouvoir de prédire qui va cliquer, acheter, mentir ou mourir](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## Livres pratique
- [ ] [Machine Learning pour les Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository(R)](https://github.com/johnmyleswhite/ML_for_Hackers)
- [GitHub repository(Python)](https://github.com/carljv/Will_it_Python)
- [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Introduction au Machine Learning avec Python: Un Guide pour Data Scientists](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Documentation pédagogique
- [Diapositives pour Chapitre 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [Diapositives pour Chapitres 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning en Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [Reactive Machine Learning Systems(MEAP)](https://www.manning.com/books/reactive-machine-learning-systems)
- [GitHub repository](https://github.com/jeffreyksmithjr/reactive-machine-learning-systems)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
- [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html)
- [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python)
- [Vidéos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
- [ ] [Construire des systèmes de Machine Learning en Python](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python)
- [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)
- [ ] [Learning scikit-learn: Machine Learning in Python](https://www.packtpub.com/big-data-and-business-intelligence/learning-scikit-learn-machine-learning-python)
- [GitHub repository](https://github.com/gmonce/scikit-learn-book)
- [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
- [ ] [Probabilistic Graphical Models: Principles and Techniques](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193)
- [ ] [Machine Learning: Hands-On for Developers and Technical Professionals](https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061)
- [Machine Learning Hands-On for Developers and Technical Professionals review](https://blogs.msdn.microsoft.com/querysimon/2015/01/01/book-review-machine-learning-hands-on-for-developers-and-technical-professionals/)
- [GitHub repository](https://github.com/jasebell/mlbook)
- [ ] [Learning from Data](https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069)
- [Online tutorials](https://work.caltech.edu/telecourse.html)
- [ ] [Reinforcement Learning: An Introduction (2nd Edition)](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)
- [GitHub repository](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
- [ ] [Machine Learning with TensorFlow(MEAP)](https://www.manning.com/books/machine-learning-with-tensorflow)
- [GitHub repository](https://github.com/BinRoot/TensorFlow-Book)
## Les Compétitions Kaggle
- [ ] [Les compétitions Kaggle: Comment et par où commencer ?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [Comment un débutant a utilisé de petits projets pour apprendre le Machine Learning et participer à Kaggle](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle)
- [ ] [Maîtriser Kaggle en participant sans cesse](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## Séries vidéos
- [ ] [Machine Learning pour Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Recettes de Machine Learning avec Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [Tout ce dont vous avez besoin de savoir sur le Machine Learning en 30 minutes ou moins](https://vimeo.com/43547079)
- [ ] [Une Introduction amicale au Machine Learning](https://www.youtube.com/watch?v=IpGxLWOIZy4)
- [ ] [Toute les bases pour appliquer le Deep Learning - Andrew Ng](https://www.youtube.com/watch?v=F1ka6a13S9I)
- [ ] BigML Webinar
- [Vidéo](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo)
- [Resources](https://bigml.com/releases)
- [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA)
- [ ] [Machine learning en Python avec scikit-learn](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A)
- [GitHub repository](https://github.com/justmarkham/scikit-learn-videos)
- [Blog](http://blog.kaggle.com/author/kevin-markham/)
- [ ] [My playlist – Top YouTube Videos on Machine Learning, Neural Network & Deep Learning](https://www.analyticsvidhya.com/blog/2015/07/top-youtube-videos-machine-learning-neural-network-deep-learning/)
- [ ] [16 Nouveaux tutoriels à voir absolument, cours de Machine Learning](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/)
- [ ] [DeepLearning.TV](https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ)
- [ ] [Learning To See](https://www.youtube.com/playlist?list=PLiaHhY2iBX9ihLasvE8BKnS2Xg8AhY6iV)
- [ ] [Neural networks class - Université de Sherbrooke](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
- [ ] [21 Vidéos de Deep Learning, tutoriels et cours sur Youtube datant de 2016](https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/)
- [ ] [30 Meilleures vidéos, tutoriels et cours de Machine Learning et Intelligence Artificiel datant de 2016](https://www.analyticsvidhya.com/blog/2016/12/30-top-videos-tutorials-courses-on-machine-learning-artificial-intelligence-from-2016/)
- [ ] [Practical Deep Learning For Coders](http://course.fast.ai/index.html)
- [ ] [Practical Deep Learning For Coders Version 2 (PyTorch)](http://forums.fast.ai/t/welcome-to-part-1-v2/5787)
## MOOC
- [ ] [edX's Introduction à l'Intelligence Artificielle (IA)](https://www.edx.org/course/introduction-artificial-intelligence-ai-microsoft-dat263x)
- [ ] [Intro au Machine Learning par Udacity](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [Revue de Intro au Machine Learning par Udacity](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Supervised, Unsupervised et Renforcement par Udacity](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Fondations: Aborder un cas d'étude](https://www.coursera.org/learn/ml-foundations)
- [ ] [Machine Learning et IA Fondations: Estimation de valeurs](https://www.lynda.com/Data-Science-tutorials/Machine-Learning-Essential-Training-Value-Estimations/548594-2.html)
- [ ] [Hands-On Data Science Education par Kaggle](https://www.kaggle.com/learn/overview)
- [ ] [Programme professionnel pour l'Intelligence Artificel par Microsoft ](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/)
- [ ] [Machine Learning par Coursera](https://www.coursera.org/learn/machine-learning)
- [Vidéo seulement](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [Revue de Machine Learning par Coursera](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera: Le plan détaillé sur le Machine Learning](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
- [ ] [Machine Learning Distilled](https://code.tutsplus.com/courses/machine-learning-distilled)
- [ ] [BigML training](https://bigml.com/training)
- [ ] [Neural Networks pour Machine Learning par Coursera](https://www.coursera.org/learn/neural-networks)
- Enseigné par Geoffrey Hinton, une pionnier dans le domaine des réseaux de neurones.
- [ ] [Machine Learning - CS - Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [Applications créatives du Deep Learning par TensorFlow](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info)
- [ ] [Intro aux Statistiques descriptives](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
- [ ] [Intro aux Inférences statistiques](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)
- [ ] [6.S094: Deep Learning pour voitures autonomes](http://selfdrivingcars.mit.edu/)
- [ ] [6.S191: Introduction au Deep Learning](http://introtodeeplearning.com/index.html)
- [ ] [Deep Learning par Coursera](https://www.coursera.org/specializations/deep-learning)
## Ressources
- [ ] [Le tout début de l'apprentissage en Machine Learning](https://hackernoon.com/absolute-beginning-into-machine-learning-e90ceda5a4bc)
- [ ] [Apprendre le Machine Learning en un mois seulement](https://elitedatascience.com/machine-learning-masterclass)
- [ ] [Le Guide accessible du Machine Learning et de l'Intelligence Artificiel](https://medium.com/@samdebrule/a-humans-guide-to-machine-learning-e179f43b67a0#.cpzf3a5c0)
- [ ] [Les ressources conservées par un communauté de programmeur pour apprendre le Machine Learning](https://hackr.io/tutorials/learn-machine-learning-ml)
- [ ] [Livre des meilleures pratiques en Machine Learning par Google](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [ ] [Machine Learning pour les Ingénieurs Logiciel sur Hacker News](https://news.ycombinator.com/item?id=12898718)
- [ ] [Machine Learning pour Développeurs](https://xyclade.github.io/MachineLearning/)
- [ ] [Machine Learning pour les Humains🤖👶](https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12)
- [ ] [Conseils de Machine Learning pour Développeurs](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [Machine Learning pour les nuls](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [Commencer en Machine Learning: Pour tout les niveaux de débutant à master](https://medium.com/@suffiyanz/getting-started-with-machine-learning-f15df1c283ea#.yjtiy7ei9)
- [ ] [Comment apprendre le Machine Learning: La méthode autodidacte](https://elitedatascience.com/learn-machine-learning)
- [ ] [Ressource pour étudier le Machine Learning soi-même](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] [Un Guide sincère sur le Machine Learning](https://medium.com/axiomzenteam/an-honest-guide-to-machine-learning-2f6d7a6df60e#.ib12a1yw5)
- [ ] Assez de Machine Learning pour rendre sa lisibilité à Hacker News
- [Video](https://www.youtube.com/watch?v=O7IezJT9uSI)
- [Diapositive](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely)
- [ ] [Plonger dans le Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)
- [ ] [{Machine, Deep} Apprentissage pour ingénieurs logiciel](https://speakerdeck.com/pmigdal/machine-deep-learning-for-software-engineers)
- [ ] [Deep Learning pour débutant](https://deeplearning4j.org/deeplearningforbeginners.html)
- [ ] [Les bases du Deep Learning](https://github.com/pauli-space/foundations_for_deep_learning)
- [ ] [Machine Learning Carte mentale / Aide mémoire](https://github.com/dformoso/machine-learning-mindmap)
- Cours de Machine Learning dans les Universités
- [ ] [Stanford](http://ai.stanford.edu/courses/)
- [ ] [Machine Learning Summer Schools](http://mlss.cc/)
- [ ] [Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [Cambridge](http://mlg.eng.cam.ac.uk/)
- Sujets Flipboard
- [Machine learning](https://flipboard.com/topic/machinelearning)
- [Deep learning](https://flipboard.com/topic/deeplearning)
- [Artificial Intelligence](https://flipboard.com/topic/artificialintelligence)
- Sujets Medium
- [Machine learning](https://medium.com/tag/machine-learning/latest)
- [Deep learning](https://medium.com/tag/deep-learning)
- [Artificial Intelligence](https://medium.com/tag/artificial-intelligence)
- Top 10 Mensuel des articles
- [Machine Learning](https://medium.mybridge.co/search?q=%22Machine%20Learning%22)
- [Algorithms](https://medium.mybridge.co/search?q=Algorithms)
- [Liste exhaustive de ressources sur la Sciences des données](http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources)
- [Ressources sur l'Intelligence Articificiel par DigitalMind](http://blog.digitalmind.io/post/artificial-intelligence-resources)
- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)
- [Machine Learning par CreativeAi](http://www.creativeai.net/?cat%5B0%5D=machine-learning)
## Jeux
- [Halite: A.I. Coding Game](https://halite.io/)
- [Vindinium: A.I. Programming Challenge](http://vindinium.org/)
- [General Video Game AI Competition](http://www.gvgai.net/)
- [Angry Birds AI Competition](https://aibirds.org/)
- [The AI Games](http://theaigames.com/)
- [Fighting Game AI Competition](http://www.ice.ci.ritsumei.ac.jp/~ftgaic/)
- [CodeCup](http://www.codecup.nl/intro.php)
- [Student StarCraft AI Tournament](http://sscaitournament.com/)
- [AIIDE StarCraft AI Competition](http://www.cs.mun.ca/~dchurchill/starcraftaicomp/)
- [CIG StarCraft AI Competition](https://sites.google.com/site/starcraftaic/)
- [CodinGame - AI Bot Games](https://www.codingame.com/training/machine-learning)
## Participer aux projets Open-source
- [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet)
## Podcasts
- ### Podcasts pour Débutant:
- [Talking Machines](http://www.thetalkingmachines.com/)
- [Linear Digressions](http://lineardigressions.com/)
- [Data Skeptic](http://dataskeptic.com/)
- [This Week in Machine Learning & AI](https://twimlai.com/)
- [Machine Learning Guide](http://ocdevel.com/podcasts/machine-learning)
- ### "Plus" de podcasts pour aller plus loin
- [Partially Derivative](http://partiallyderivative.com/)
- [O’Reilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast)
- [Not So Standard Deviation](https://soundcloud.com/nssd-podcast)
- ### Podcasts pour sortir des sentiers battus:
- [Data Stories](http://datastori.es/)
## Communautés
- Quora
- [Machine Learning](https://www.quora.com/topic/Machine-Learning)
- [Statistics](https://www.quora.com/topic/Statistics-academic-discipline)
- [Data Mining](https://www.quora.com/topic/Data-Mining)
- Reddit
- [Machine Learning](https://www.reddit.com/r/machinelearning)
- [Computer Vision](https://www.reddit.com/r/computervision)
- [Natural Language](https://www.reddit.com/r/languagetechnology)
- [Data Science](https://www.reddit.com/r/datascience)
- [Big Data](https://www.reddit.com/r/bigdata)
- [Statistics](https://www.reddit.com/r/statistics)
- [Data Tau](http://www.datatau.com/)
- [Deep Learning News](http://news.startup.ml/)
- [KDnuggets](http://www.kdnuggets.com/)
## Conférences
- Neural Information Processing Systems ([NIPS](https://nips.cc/))
- International Conference on Learning Representations ([ICLR](http://www.iclr.cc/doku.php?id=ICLR2017:main&redirect=1))
- Association for the Advancement of Artificial Intelligence ([AAAI](http://www.aaai.org/Conferences/AAAI/aaai17.php))
- IEEE Conference on Computational Intelligence and Games ([CIG](http://www.ieee-cig.org/))
- IEEE International Conference on Machine Learning and Applications ([ICMLA](http://www.icmla-conference.org/))
- International Conference on Machine Learning ([ICML](https://2017.icml.cc/))
- International Joint Conferences on Artificial Intelligence ([IJCAI](http://www.ijcai.org/))
- Association for Computational Linguistics ([ACL](http://acl2017.org/))
## Questions aux Entretiens
- [ ] [Comment se préparer à un entretien sur le Machine Learning](http://blog.udacity.com/2016/05/prepare-machine-learning-interview.html)
- [ ] [40 Questions posées aux Startups de Machine Learning / Science des données durant un entretien](https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science)
- [ ] [21 Questions et réponse en Sciences des données à connaître absolument pour un entretien](http://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html)
- [ ] [Top 50 des questions réponses sur le Machine learning en entretien](http://career.guru99.com/top-50-interview-questions-on-machine-learning/)
- [ ] [Questions d'ingénieur sur le Machine Learning durant un entretien](https://resources.workable.com/machine-learning-engineer-interview-questions)
- [ ] [Questions récurrentes lors des entretiens en Machine Learning](http://www.learn4master.com/machine-learning/popular-machine-learning-interview-questions)
- [ ] [Quelles sont les questions souvent posées lors des entretiens en Machine Learning?](https://www.quora.com/What-are-some-common-Machine-Learning-interview-questions)
- [ ] [Quelles sont les meilleures question à poser en entretien pour estimer le niveau d'un chercheur en Machine Learning?](https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher)
- [ ] [Recueil de questions sur le Machine Learning en entretien](http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/)
- [ ] [121 Questions et réponses essentielles en Machine Learning ](https://elitedatascience.com/mlqa-reading-list)
## Les Entreprises que j'admire
- [ ] [ELSA - Your virtual pronunciation coach](https://www.elsanow.io/home)
================================================
FILE: README-pt-BR.md
================================================
# Top-down learning path: Machine Learning for Software Engineers
Inspired by [Coding Interview University](https://github.com/jwasham/coding-interview-university/blob/master/translations/README-ptbr.md).
_Se você gostou deste projeto, por favor me dê uma estrela_ ★ _e ajude a divulgar o material._ ;)
<a href="https://twitter.com/intent/tweet?text=Plano%20de%20estudos%20para%20Engenheiros%20de%20Machine%20Learning%20https://github.com/ZuzooVn/machine-learning-for-software-engineers%20por%20@zuzoovn" target="_blank">
Compartilhe no Twitter</a>
## O que é?
Este é meu plano de estudo para ir de desenvolvedor mobile (autodidata, sem diploma) para Engenheiro de Machine Learning.
Meu principal objetivo era encontrar uma abordagem para estudar Machine Learning, que é principalmente hands-on (aprender fazendo) e abstrair a maioria da matemática para o iniciante. Esta abordagem não é convencional porque ela é uma abordagem top-down e resultados-primeiro projetada para engenheiros de software.
Por favor, sinta-se livre para fazer qualquer contribuição que você achar que pode o tornar melhor.
---
## Tabela de conteúdo
- [O que é?](#o-que-é)
- [Por que usar?](#por-que-usar)
- [Como usar](#como-usar)
- [Siga-me](#siga-me)
- [Não sinta que não é inteligente o bastante](#não-sinta-que-não-é-inteligente-o-bastante)
- [Sobre Video Resources](#sobre-video-resources)
- [Conhecimento prévio](#conhecimento-prévio)
- [O Plano diário](#o-plano-diário)
- [Motivação](#motivação)
- [Visão geral do Machine Learning](#visão-geral-do-machine-learning)
- [Maestria do Machine Learning](#maestria-do-machine-learning)
- [Machine Learning é divertido](#machine-learning-é-divertido)
- [Machine learning: um guia profundo, não técnico](#machine-learning-um-guia-profundo-não-técnico)
- [Relatos e experiências](#relatos-e-experiências)
- [Livros para iniciantes](#livros-para-iniciantes)
- [Livros para prática](#livros-para-prática)
- [Competições de conhecimento Kaggle](#competições-de-conhecimento-kaggle)
- [Video Series](#video-series)
- [MOOC](#mooc)
- [Pesquisas](pesquisas)
- [Torna-se um contribuidor Open Source](#torne-se-um-contribuidor-open-sourse)
- [Communidades](#comunidades)
- [My admired companies](#my-admired-companies)
---
## Por que usar?
Eu estou seguindo este plano para me preparar para meu próximo futuro emprego: Engenheiro de Machine Learning. Venho construindo aplicativos nativos móveis (iOS/Android/Blackberry) desde 2011. Eu tenho um diploma de engenharia de Software, não um diploma de Ciência da Computação. Tenho um pouco de conhecimentos básicos sobre: cálculo, Álgebra Linear, matemática discreta, probabilidade e estatística na Universidade.
Pense sobre meu interesse em Machine Learning:
- [Posso aprender e arrumar um emprego em Machine Learning sem estudar mestrado e Phd em Ciência da Computação?](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD)
- *"Você pode, mas isto é muito mais difícil do que quando eu entrei no campo."* [Drac Smith](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD/answer/Drac-Smith?srid=oT0p)
- [Como eu consigo um emprego em Machine Learning como um programador de software que auto-estudou Machine Learning, mas nunca teve a chance de usar isso no trabalho?](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work)
- *"Estou contratando especialistas de Machine Learning para minha equipe e seu MOOC não vai conseguir para você o trabalho (há melhores notícias abaixo). Na verdade, muitas pessoas com um mestrado em Machine Learning não terão o emprego porque eles (e a maioria que tomaram MOOC) não têm uma compreensão profunda que vai me ajudar a resolver os meus problemas."* [Ross C. Taylor](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work/answer/Ross-C-Taylor?srid=oT0p)
- [Que habilidades são necessárias para trabalhos de Machine Learning?](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs)
- *"Primeiramente, você precisa ter um decente background de Ciência da Computação/Matemática. ML é um tópico avançado, então a maioria dos livros didáticos assumem que você tem esse background. Por segundo, Machine Learning é um tema muito geral com várias sub especialidades que exigem habilidades únicas. Você pode querer procurar o currículo de um programa de MS em Machine Learning para ver o curso, o currículo e livro didático."* [Uri](http://softwareengineering.stackexchange.com/a/79717)
- *"Estatística, propabilidade, computação distribuída e estatística."* [Hydrangea](http://softwareengineering.stackexchange.com/a/79575)
Eu me encontro em tempos difíceis.
AFAIK, [Há dois lados para Machine Learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/):
- Prática de Machine Learning: Isto é sobre bancos de dados de consultas, limpeza de dados, escrevendo scripts para transformar dados e colagem de algoritmo e bibliotecas juntos e escrever código personalizado para espremer respostas confiáveis de dados para satisfazer as perguntas difíceis e mal definidas. É a porcaria da realidade.
- Teoria de Machine Learning: Isto é sobre matemática e abstração e cenários idealizados e limites e beleza e informando o que é possível. É muito mais puro e mais limpo e removido da confusão da realidade.
Eu acho que a melhor maneira para metodologia centrada na prática é algo como ['prática - aprendizagem - prática'](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985), que significa onde estudantes primeiro vêm com alguns projetos existentes com problemas e soluções (prática) para se familiarizar com os métodos tradicionais na área e talvez também com sua metodologia.Depois de praticar com algumas experiências elementares, podem ir para os livros e estudar a teoria subjacente, que serve para guiar a sua futura prática avançada e reforçará a sua caixa de ferramentas de solução de problemas práticos. Estudar a teoria também melhora ainda mais sua compreensão sobre as experiências elementares e irá ajudá-los a adquirir experiências avançadas mais rapidamente.
É um plano longo. Isso vai demorar anos para mim. Se você já está familiarizado com bastante disso já, você levará muito menos tempo.
## Como usar
Tudo abaixo é uma estrutura de tópicos, e você deve enfrentar os itens em ordem de cima para baixo.
Eu estou usando o especial Markdown do Github, incluindo a lista de tarefas para verificar o progresso.
- [x] Crie um novo branch, então você poderá verificar itens como esse, apenas coloque um x entre os colchetes.
[More about Github-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Siga-me
Eu sou um engenheiro de Software vietnamita que é realmente apaixonado e quer trabalhar nos EUA.
Quanto eu trabalhei durante este plano? Aproximadamente 4 horas/noite após um dia longo no trabalho.
Eu estou na jornada.
| |
|:---:|
| USA as heck |
## Não sinta que não é inteligente o bastante
Fico desencorajado por livros e cursos que me dizem que o quanto antes eu puder, cálculo multivariável, inferencial e álgebra linear são pré-requisitos. Ainda não sei como começar...
- [What if I'm Not Good at Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/)
- [How do I learn machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
## Sobre Video Resources
Alguns vídeos estão disponíveis apenas registrando-se em uma classe Coursera ou EdX. É de graça, mas às vezes as classes já não estão em sessão, então você tem que esperar uns meses, se não, não terá acesso.
Eu vou estar adicionando mais vídeos de fontes públicas e substituindo os vídeos do curso on-line ao longo do tempo. Eu gosto de usar palestras de universidade.
## Conhecimento prévio
Esta seção curta foram pré-requisitos/informações interessantes que eu queria aprender antes de começar o plano diário.
- [ ] [What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1)
- [ ] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
- [ ] [Don't Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [ ] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## O Plano Diário
Cada assunto não requer um dia inteiro para ser capaz de compreendê-lo totalmente, e você pode fazer vários desses em um dia.
Cada dia eu pego um assunto da lista abaixo, leia de capa a capa, tome nota, faça os exercícios e escreva uma implementação em Python ou R.
# Motivação
- [ ] [Dream](https://www.youtube.com/watch?v=g-jwWYX7Jlo)
## Visão geral do Machine learning
- [ ] [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)
- [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
## Maestria do Machine learning
- [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [Machine Learning for Programmers](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [Applied Machine Learning with Machine Learning Mastery](http://machinelearningmastery.com/start-here/)
- [ ] [Python Machine Learning Mini-Course](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [Machine Learning Algorithms Mini-Course](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## Machine learning é divertido
- [ ] [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Part 2: Using Machine Learning to generate Super Mario Maker levels](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [Part 3: Deep Learning and Convolutional Neural Networks](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637)
- [ ] [Part 4: Modern Face Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc)
- [ ] [Part 5: Language Translation with Deep Learning and the Magic of Sequences](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c)
## Machine learning: um guia profundo, não técnico
- [ ] [Overview, goals, learning types, and algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [Data selection, preparation, and modeling](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [Model evaluation, validation, complexity, and improvement](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [Model performance and error analysis](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [Unsupervised learning, related fields, and machine learning in practice](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## Relatos e experiências
- [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [Learning Path : Your mentor to become a machine learning expert](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [You Too Can Become a Machine Learning Rock Star! No PhD](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7)
- [ ] How to become a Data Scientist in 6 months: A hacker’s approach to career planning
- [Video](https://www.youtube.com/watch?v=rIofV14c0tc)
- [Slide](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [5 Skills You Need to Become a Machine Learning Engineer](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html)
- [ ] [Are you a self-taught machine learning engineer? If yes, how did you do it & how long did it take you?](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you)
- [ ] [How can one become a good machine learning engineer?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
## Livros para iniciantes
- [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [Data Science for Business: What you need to know about data mining and data analytic-thinking](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)
- [ ] [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## Livros para prática
- [ ] [Machine Learning for Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository](https://github.com/johnmyleswhite/ML_for_Hackers)
- [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Introduction to Machine Learning with Python: A Guide for Data Scientists](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Teaching material
- [Slides for Chapters 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [Slides for Chapters 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
## Competições de conhecimento Kaggle
- [ ] [Kaggle Competitions: How and where to begin?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [How a Beginner Used Small Projects To Get Started in Machine Learning and Compete on Kaggle](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle)
- [ ] [Master Kaggle By Competing Consistently](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## Video Series
- [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [Everything You Need to know about Machine Learning in 30 Minutes or Less](https://vimeo.com/43547079)
## MOOC
- [ ] [Udacity's Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [Udacity Intro to Machine Learning Review](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Udacity's Supervised, Unsupervised & Reinforcement](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)
- [ ] [Coursera's Machine Learning](https://www.coursera.org/learn/machine-learning)
- [Video only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [Coursera Machine Learning review](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera: Machine Learning Roadmap](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
## Recursos
- [Cursos Online de Machine Learning](https://pt-br.classpert.com/machine-learning)
## Pesquisas
- [ ] [Machine Learning for Developers](https://xyclade.github.io/MachineLearning/)
- [ ] [Machine Learning Advice for Developers](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [Machine Learning For Complete Beginners](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] [Enough Machine Learning to Make Hacker News Readable Again](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely)
## Torne-se um contribuidor Open Sourse
- [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet)
## Comunidades
- ### Quora
- [Machine Learning](https://www.quora.com/topic/Machine-Learning)
- [Statistics](https://www.quora.com/topic/Statistics-academic-discipline)
- [Data Mining](https://www.quora.com/topic/Data-Mining)
- ### Reddit
- [Machine Learning](https://www.reddit.com/r/machinelearning)
- ### [Data Tau](http://www.datatau.com/)
## My admired companies
- [ ] [ELSA - Your virtual pronunciation coach](https://www.elsanow.io/home)
================================================
FILE: README-zh-CN.md
================================================
# 自上而下的学习路线: 软件工程师的机器学习
灵感来源于 [谷歌面试学习手册](https://github.com/jwasham/coding-interview-university/blob/master/translations/README-cn.md)
> * 原文地址：[Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
> * 原文作者：[ZuzooVn(Nam Vu)](https://github.com/ZuzooVn)
> * 翻译：[lsvih](https://github.com/lsvih)
## 这是？
这是本人为期数月的学习计划。我正要从一名移动端软件开发者(自学，无计科文凭)转型成为一名机器学习工程师。
我的主要目标是找到一种以实践为主的学习方法，并为初学者抽象掉大多数的数学概念。
这种学习方法是非传统的，因为它是专门为软件工程师所设计的自上而下、以结果为导向的学习方法。
如果您想让它更好的话，随时欢迎您的贡献。
---
## 目录
- [这是？](#这是)
- [为何要用到它？](#为何要用到它)
- [如何使用它？](#如何使用它)
- [Follow me](#follow-me)
- [别认为自己不够聪明](#别认为自己不够聪明)
- [关于视频资源](#关于视频资源)
- [预备知识](#预备知识)
- [每日计划](#每日计划)
- [动机](#动机)
- [机器学习概论](#机器学习概论)
- [掌握机器学习](#掌握机器学习)
- [有趣的机器学习](#有趣的机器学习)
- [机器学习简介](#机器学习简介)
- [一本深入的机器学习指南](#一本深入的机器学习指南)
- [故事与经验](#故事与经验)
- [机器学习算法](#机器学习算法)
- [入门书籍](#入门书籍)
- [实用书籍](#实用书籍)
- [Kaggle知识竞赛](#kaggle知识竞赛)
- [系列视频](#系列视频)
- [MOOC](#mooc)
- [资源](#资源)
- [成为一名开源贡献者](#成为一名开源贡献者)
- [游戏](#游戏)
- [播客](#播客)
- [社区](#社区)
- [相关会议](#相关会议)
- [面试问题](#面试问题)
- [我崇拜的公司](#我崇拜的公司)
---
## 为何要用到它？
我会为了我未来的工作————机器学习工程师 遵循这份计划。自2011年以来，我一直进行着移动端应用的开发(包括安卓、iOS与黑莓)。我有软件工程的文凭，但没有计算机科学的文凭。我仅仅在大学的时候学习过一点基础科学，包括微积分、线性代数、离散数学、概率论与统计。
我认真思考过我在机器学习方面的兴趣:
- [我能在没有计科硕士、博士文凭的情况下找到一份关于机器学习的工作吗？](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD)
- *"你当然可以，但是我想进入这个领域则无比艰难。"* [Drac Smith](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD/answer/Drac-Smith?srid=oT0p)
- [我是一名软件工程师，我自学了机器学习，我如何在没有相关经验的情况下找到一份关于机器学习的工作？](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work)
- *"我正在为我的团队招聘机器学习专家，但你的MOOC并不会给你带来工作机会。事实上，大多数机器学习方向的硕士也并不会得到工作机会，因为他们（与大多数上过MOOC的人一样）并没有深入地去理解。他们都没法帮助我的团队解决问题。"* [Ross C. Taylor](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work/answer/Ross-C-Taylor?srid=oT0p)
- [找一份机器学习相关的工作需要掌握怎样的技能？](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs)
- *"首先，你得有正儿八经的计科或数学专业背景。ML是一个比较先进的课题，大多数的教材都会直接默认你有以上背景。其次，机器学习是一个集成了许多子专业的奇技淫巧的课题，你甚至会想看看MS的机器学习课程，去看看他们的授课、课程和教材。"* [Uri](http://softwareengineering.stackexchange.com/a/79717)
- *"统计，假设，分布式计算，然后继续统计。"* [Hydrangea](http://softwareengineering.stackexchange.com/a/79575)
我深陷困境。
据我所知, [机器学习有两个方向](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/)：
- 实用机器学习： 这个方向主要是查询数据库、数据清洗、写脚本来转化数据，把算法和库结合起来再加上一些定制化的代码，从数据中挤出一些准确的答案来证明一些困难且模糊不清的问题。实际上它非常混乱。
- 理论机器学习： 这个方向主要是关于数学、抽象、理想状况、极限条件、典型例子以及一切可能的特征。这个方向十分的干净、整洁，远离混乱的现实。
我认为对于以实践为主的人来说，做好的方法就是 [“练习--学习--练习”](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985)，这意味着每个学生一开始就能参与一些现有项目与一些问题，并练习（解决）它们以熟悉传统的方法是怎么做的。在有了一些简单的练习经验之后，他们就可以开始钻进书里去学习理论知识。这些理论知识将帮助他们在将来进行更进一步的训练，充实他们解决实际问题的工具箱。学习理论知识还会加深他们对那些简单练习的理解，帮助他们更快地获得进阶的经验。
这是一个很长的计划，它花去了我一年的时间。如果你已经对它有所了解了，它将会让你省去很多时间。
## 如何使用它？
以下的内容全部是概要，你需要从上往下来解决这些项目。
我使用的是Github独特的flavored markdown的任务列表来检查我计划的进展。
- [x] 创建一个新的分支，然后你可以这样来标出你已经完成的项目，只需要在框中填写一个x即可：[x]
[了解更多有关 Github-flavored markdown的知识](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Follow me
我是一名非常非常想去美国工作的越南软件工程师。
我在这份计划中花多少时间？在每天的艰辛工作完成后，每晚花4小时。
我已经在实现梦想的旅途中了。
- Twitter: [@Nam Vu](https://twitter.com/zuzoovn)
| |
|:---:|
| USA as heck |
## 别认为自己不够聪明
当我打开书本，发现他们告诉我多元微积分、统计与推理、线性代数是学习机器学习的先决条件的时候，我非常沮丧。因为我不知道从哪儿开始…
- [我数学不好怎么办](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [没有数学专业背景而理解机器学习算法的5种技巧](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/)
- [我是如何学习机器学习的？](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
## 关于视频资源
部分视频只有在Coursera、EdX的课程注册了才能观看。虽然它们是免费的，但有些时间段这些课程并不开放，你可能需要等上一段时间(可能是好几个月)。我将会加上更多的公开的视频源来代替这些在线课程的视频。我很喜欢大学的讲座。
## 预备知识
这个小章节是一些在每日计划开始前我想去了解的一些预备知识与一些有趣的信息。
- [ ] [Data Analytics，Data Analysis，数据挖掘，数据科学，机器学习，大数据的区别是什么？](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1)
- [ ] [学习如何去学习](https://www.coursera.org/learn/learning-how-to-learn)
- [ ] [不要斩断锁链](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [ ] [如何自学](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## 每日计划
每个主题都不需要用一整天来完全理解它们，你可以每天完成它们中的多个。
每天我都会从下面的列表中选一个出来，一遍又一遍的读，做笔记，练习，用Python或R语言实现它。
# 动机
- [ ] [梦](https://www.youtube.com/watch?v=g-jwWYX7Jlo)
## 机器学习概论
- [ ] [形象的机器学习简介](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [一份温柔的机器学习指南](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)
- [ ] [为开发者准备的机器学习简介](http://blog.algorithmia.com/introduction-machine-learning-developers/)
- [ ] [菜鸟的机器学习基础](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
- [ ] [你如何向非计算机专业的人来解释机器学习与数据挖掘？](https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people)
- [ ] [在罩子下的机器学习，博文简单明了地介绍了机器学习的原理](https://georgemdallas.wordpress.com/2013/06/11/big-data-data-mining-and-machine-learning-under-the-hood/)
- [ ] [机器学习是什么？它是如何工作的呢？](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1)
- [ ] [深度学习——一份非技术性的简介](http://www.slideshare.net/AlfredPong1/deep-learning-a-nontechnical-introduction-69385936)
## 掌握机器学习
- [ ] [掌握机器学习的方法](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [程序员的机器学习](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [掌握并运用机器学习](http://machinelearningmastery.com/start-here/)
- [ ] [Python机器学习小课程](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [机器学习算法小课程](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## 有趣的机器学习
- [ ] [机器学习真有趣！](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Part 2: 使用机器学习来创造超级马里奥的关卡](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [Part 3: 深度学习与卷积神经网络](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637)
- [ ] [Part 4: 现代人脸识别与深度学习](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc)
- [ ] [Part 5: 翻译与深度学习和序列的魔力](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c)
- [ ] [Part 6: 如何使用深度学习进行语音识别](https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a#.lhr1nnpcy)
- [ ] [Part 7: 使用生成式对抗网络创造 8 像素艺术](https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7)
- [ ] [Part 8: 如何故意欺骗神经网络](https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196)
## [机器学习简介](https://triskell.github.io/2016/11/15/Inky-Machine-Learning.html)(用手指沾上墨水来书写机器学习简介)
- [ ] [Part 1 : 什么是机器学习？](https://triskell.github.io/2016/10/23/What-is-Machine-Learning.html)
- [ ] [Part 2 : 监督学习与非监督学习](https://triskell.github.io/2016/11/13/Supervised-Learning-and-Unsupervised-Learning.html)
## 一本深入的机器学习指南
- [ ] [概述，目标，学习类型和算法](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [数据的选择，准备与建模](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [模型的评估，验证，复杂性与改进](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [模型性能与误差分析](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [无监督学习，相关领域与实践中的机器学习](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## 故事与经验
- [ ] [一周的机器学习](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [一年的机器学习](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [我是如何在3天内写出我的第一个机器学习程序的](http://blog.adnansiddiqi.me/how-i-wrote-my-first-machine-learning-program-in-3-days/)
- [ ] [学习路径：你成为机器学习专家的导师](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [不是PhD你也可以成为机器学习的摇滚明星](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7)
- [ ] 如何6个月成为一名数据科学家：一名黑客的职业规划
- [视频](https://www.youtube.com/watch?v=rIofV14c0tc)
- [幻灯片](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [5个你成为机器学习工程师必须要掌握的技能](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html)
- [ ] [你是一个自学成才的机器学习工程师吗？你是怎么做的？花了多长时间？](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you)
- [ ] [一个人如何成为一名优秀的机器学习工程师？](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
- [ ] [一个专注于机器学习的学术假期](http://karlrosaen.com/ml/)
## 机器学习算法
- [ ] [用“士兵”来表示10种机器学习算法](https://www.analyticsvidhya.com/blog/2015/12/10-machine-learning-algorithms-explained-army-soldier/)
- [ ] [Top10的数据挖掘算法](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/)
- [ ] [介绍10种机器学习的术语](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/)
- [ ] [机器学习算法之旅](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
- [ ] [机器学习工程师需要知道的10种算法](https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.ofc7t2965)
- [ ] [比较监督学习算法](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
- [收集的最简化、可执行的机器学习算法](https://github.com/rushter/MLAlgorithms)
## 入门书籍
- [ ] [《Data Smart: Using Data Science to Transform Information into Insight》第 1 版](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [《Data Science for Business: What you need to know about data mining and data analytic-thinking》](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)
- [ ] [《Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die》](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## 实用书籍
- [ ] [Hacker 的机器学习](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository(R)](https://github.com/johnmyleswhite/ML_for_Hackers)
- [GitHub repository(Python)](https://github.com/carljv/Will_it_Python)
- [ ] [Python 机器学习](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [集体智慧编程: 创建智能 Web 2.0 应用](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [机器学习: 算法视角，第二版](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Python 机器学习简介: 数据科学家指南](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [数据挖掘: 机器学习工具与技术实践，第 3 版](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Teaching material
- [1-5 章幻灯片(zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [6-8 章幻灯片(zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [Reactive Machine Learning Systems(MEAP)](https://www.manning.com/books/reactive-machine-learning-systems)
- [GitHub repository](https://github.com/jeffreyksmithjr/reactive-machine-learning-systems)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
- [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html)
- [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python)
- [视频](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
- [ ] [使用 Python 构建机器学习系统](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python)
- [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)
- [ ] [学习 scikit-learn: 用 Python 进行机器学习](https://www.packtpub.com/big-data-and-business-intelligence/learning-scikit-learn-machine-learning-python)
- [GitHub repository](https://github.com/gmonce/scikit-learn-book)
- [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
- [ ] [Probabilistic Graphical Models: Principles and Techniques](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193)
- [ ] [Machine Learning: Hands-On for Developers and Technical Professionals](https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061)
- [Machine Learning Hands-On for Developers and Technical Professionals review](https://blogs.msdn.microsoft.com/querysimon/2015/01/01/book-review-machine-learning-hands-on-for-developers-and-technical-professionals/)
- [GitHub repository](https://github.com/jasebell/mlbook)
- [ ] [从数据中学习](https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069)
- [在线教程](https://work.caltech.edu/telecourse.html)
- [ ] [强化学习——简介(第 2 版)](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)
- [GitHub repository](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
- [ ] [使用TensorFlow(MEAP)进行机器学习](https://www.manning.com/books/machine-learning-with-tensorflow)
- [GitHub repository](https://github.com/BinRoot/TensorFlow-Book)
## Kaggle知识竞赛
- [ ] [Kaggle竞赛：怎么样，在哪里开始？](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [一个初学者如何用一个小项目在机器学习入门并在Kaggle竞争](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle)
- [ ] [如何竞争Kaggle的Master](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## 系列视频
- [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Josh Gordon 的机器学习菜谱](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [在 30 分钟以内了解机器学习的一切](https://vimeo.com/43547079)
- [ ] [一份友好的机器学习简介](https://www.youtube.com/watch?v=IpGxLWOIZy4)
- [ ] [Nuts and Bolts of Applying Deep Learning - Andrew Ng](https://www.youtube.com/watch?v=F1ka6a13S9I)
- [ ] BigML Webinar
- [视频](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo)
- [资源](https://bigml.com/releases)
- [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA)
- [ ] [Machine learning in Python with scikit-learn](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A)
- [GitHub repository](https://github.com/justmarkham/scikit-learn-videos)
- [博客](http://blog.kaggle.com/author/kevin-markham/)
- [ ] [播放清单 - YouTuBe 上最热门的机器学习、神经网络、深度学习视频](https://www.analyticsvidhya.com/blog/2015/07/top-youtube-videos-machine-learning-neural-network-deep-learning/)
- [ ] [16 个必看的机器学习教程](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/)
- [ ] [DeepLearning.TV](https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ)
- [ ] [Learning To See](https://www.youtube.com/playlist?list=PLiaHhY2iBX9ihLasvE8BKnS2Xg8AhY6iV)
- [ ] [神经网络课程 - Université de Sherbrooke](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
- [ ] [2016年的21个深度学习视频课程](https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/)
- [ ] [2016年的30个顶级的机器学习与人工智能视频教程 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016](https://www.analyticsvidhya.com/blog/2016/12/30-top-videos-tutorials-courses-on-machine-learning-artificial-intelligence-from-2016/)
- [ ] [程序员的深度学习实战](http://course.fast.ai/index.html)
## MOOC
- [ ] [edX 的人工智能导论](https://www.edx.org/course/introduction-artificial-intelligence-ai-microsoft-dat263x)
- [ ] [Udacity的机器学习导论](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [复习Udacity机器学习导论](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Udacity的监督学习、非监督学习及深入](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)
- [ ] [Coursera的机器学习](https://www.coursera.org/learn/machine-learning)
- [视频](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [复习Coursera机器学习](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera的机器学习路线图](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
- [ ] [机器学习提纯](https://code.tutsplus.com/courses/machine-learning-distilled)
- [ ] [BigML training](https://bigml.com/training)
- [ ] [Coursera的神经网络课程](https://www.coursera.org/learn/neural-networks)
- 由Geoffrey Hinton（神经网络的先驱）执教
- [ ] [使用TensorFlow创建深度学习应用](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info)
- [ ] [描述统计学概论](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
- [ ] [推理统计学概论](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)
- [ ] [6.S094: 自动驾驶的深度学习](http://selfdrivingcars.mit.edu/)
- [ ] [6.S191: 深度学习简介](http://introtodeeplearning.com/index.html)
- [ ] [Coursera 深度学习教程](https://www.coursera.org/specializations/deep-learning)
## 资源
- [ ] [一个月学会机器学习](https://elitedatascience.com/machine-learning-masterclass)
- [ ] [一份“非技术性”的机器学习与人工智能指南](https://medium.com/@samdebrule/a-humans-guide-to-machine-learning-e179f43b67a0#.cpzf3a5c0)
- [ ] [Google机器学习工程师最佳实践教程](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [ ] [Hacker News的《软件工程师的机器学习》](https://news.ycombinator.com/item?id=12898718)
- [ ] [开发者的机器学习](https://xyclade.github.io/MachineLearning/)
- [ ] [为人类🤖👶准备的机器学习](https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12)
- [ ] [给开发者的关于机器学习的建议](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [机器学习入门](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [为新手准备的机器学习入门教程](https://medium.com/@suffiyanz/getting-started-with-machine-learning-f15df1c283ea#.yjtiy7ei9)
- [ ] [初学者如何自学机器学习](https://elitedatascience.com/learn-machine-learning)
- [ ] [机器学习自学资源](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [提升你的机器学习技能](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] [一份'坦诚'的机器学习指南](https://medium.com/axiomzenteam/an-honest-guide-to-machine-learning-2f6d7a6df60e#.ib12a1yw5)
- [ ] 用机器学习让Hacker News更具可读性
- [视频](https://www.youtube.com/watch?v=O7IezJT9uSI)
- [幻灯片](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely)
- [ ] [深入机器学习](https://github.com/hangtwenty/dive-into-machine-learning)
- [ ] [软件工程师的{机器、深度}学习](https://speakerdeck.com/pmigdal/machine-deep-learning-for-software-engineers)
- [ ] [深度学习入门](https://deeplearning4j.org/deeplearningforbeginners.html)
- [ ] [深度学习基础](https://github.com/pauli-space/foundations_for_deep_learning)
- [ ] [机器学习思维导图/小抄](https://github.com/dformoso/machine-learning-mindmap)
- 大学中的机器学习课程
- [ ] [斯坦福](http://ai.stanford.edu/courses/)
- [ ] [机器学习夏令营](http://mlss.cc/)
- [ ] [牛津](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [剑桥](http://mlg.eng.cam.ac.uk/)
- Flipboard的主题
- [机器学习](https://flipboard.com/topic/machinelearning)
- [深度学习](https://flipboard.com/topic/deeplearning)
- [人工智能](https://flipboard.com/topic/artificialintelligence)
- Medium的主题
- [机器学习](https://medium.com/tag/machine-learning/latest)
- [深度学习](https://medium.com/tag/deep-learning)
- [人工智能](https://medium.com/tag/artificial-intelligence)
- 每月文章Top10
- 机器学习
- [2016年7月](https://medium.mybridge.co/top-ten-machine-learning-articles-for-the-past-month-9c1202351144#.lyycen64y)
- [2016年8月](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-2f3cb815ffed#.i9ee7qkjz)
- [2016年9月](https://medium.mybridge.co/machine-learning-top-10-in-september-6838169e9ee7#.4jbjcibft)
- [2016年10月](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-35c37825a943#.td5im1p5z)
- [2016年11月](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-b499e4213a34#.7k39i08tv)
- [2016年](https://medium.mybridge.co/machine-learning-top-10-of-the-year-v-2017-7552599935c0#.wtx2mchqn)
- 算法
- [2016年9月](https://medium.mybridge.co/algorithm-top-10-articles-in-september-8a0e6afb0807#.hgjzuyxdb)
- [2016年10月-11月](https://medium.mybridge.co/algorithm-top-10-articles-v-november-e73cba2fa87e#.kothimkhb)
- [全面的数据科学家的资源](http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources)
- [DigitalMind的人工智能资源](http://blog.digitalmind.io/post/artificial-intelligence-resources)
- [令人惊叹的机器学习](https://github.com/josephmisiti/awesome-machine-learning)
- [CreativeAi的机器学习](http://www.creativeai.net/?cat%5B0%5D=machine-learning)
## 成为一名开源贡献者
- [ ] [tensorflow/magenta: Magenta: 用机器智能生成音乐与艺术](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: 使用数据流图进行计算进行可扩展的机器学习](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: 使用深层神经网络进行面部识别](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: 神经网络模型语法](https://github.com/tensorflow/models/tree/master/syntaxnet)
## 游戏
- [Halite：AI编程游戏](https://halite.io/)
- [Vindinium: 挑战AI编程](http://vindinium.org/)
- [Video Game AI比赛](http://www.gvgai.net/)
- [愤怒的小鸟AI比赛](https://aibirds.org/)
- [The AI Games](http://theaigames.com/)
- [Fighting Game AI Competition](http://www.ice.ci.ritsumei.ac.jp/~ftgaic/)
- [CodeCup](http://www.codecup.nl/intro.php)
- [星际争霸AI学生锦标赛](http://sscaitournament.com/)
- [AIIDE星际争霸AI竞赛](http://www.cs.mun.ca/~dchurchill/starcraftaicomp/)
- [CIG星际争霸AI竞赛](https://sites.google.com/site/starcraftaic/)
- [CodinGame - AI Bot Games](https://www.codingame.com/training/machine-learning)
## 播客
- ### 适合初学者的播客：
- [Talking Machines](http://www.thetalkingmachines.com/)
- [Linear Digressions](http://lineardigressions.com/)
- [Data Skeptic](http://dataskeptic.com/)
- [This Week in Machine Learning & AI](https://twimlai.com/)
- ### “更多”进阶的播客：
- [Partially Derivative](http://partiallyderivative.com/)
- [O’Reilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast)
- [Not So Standard Deviation](https://soundcloud.com/nssd-podcast)
- ### 盒子外的播客：
- [Data Stories](http://datastori.es/)
## 社区
- Quora
- [机器学习](https://www.quora.com/topic/Machine-Learning)
- [统计学](https://www.quora.com/topic/Statistics-academic-discipline)
- [数据挖掘](https://www.quora.com/topic/Data-Mining)
- Reddit
- [机器学习](https://www.reddit.com/r/machinelearning)
- [计算机视觉](https://www.reddit.com/r/computervision)
- [自然语言处理](https://www.reddit.com/r/languagetechnology)
- [数据科学](https://www.reddit.com/r/datascience)
- [大数据](https://www.reddit.com/r/bigdata)
- [统计学](https://www.reddit.com/r/statistics)
- [Data Tau](http://www.datatau.com/)
- [Deep Learning News](http://news.startup.ml/)
- [KDnuggets](http://www.kdnuggets.com/)
## 相关会议
- ([NIPS](https://nips.cc/))
- ([ICLR](http://www.iclr.cc/doku.php?id=ICLR2017:main&redirect=1))
- ([AAAI](http://www.aaai.org/Conferences/AAAI/aaai17.php))
- ([IEEE CIG](http://www.ieee-cig.org/))
- ([IEEE ICMLA](http://www.icmla-conference.org/))
- ([ICML](https://2017.icml.cc/))
## 面试问题
- [ ] [如何准备机器学习职位的面试](http://blog.udacity.com/2016/05/prepare-machine-learning-interview.html)
- [ ] [40个机器学习与数据科学的面试问题](https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science)
- [ ] [21个必须要知道的数据科学问题与回答](http://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html)
- [ ] [Top 50 机器学习面试问题与回答](http://career.guru99.com/top-50-interview-questions-on-machine-learning/)
- [ ] [机器学习面试问题](https://resources.workable.com/machine-learning-engineer-interview-questions)
- [ ] [常用的机器学习面试问题](http://www.learn4master.com/machine-learning/popular-machine-learning-interview-questions)
- [ ] [机器学习面试问题有哪些相同的？](https://www.quora.com/What-are-some-common-Machine-Learning-interview-questions)
- [ ] [什么是评价一个机器学习研究者的最好的问题？](https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher)
- [ ] [机器学习面试问题大搜集](http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/)
- [ ] [121个需要掌握的问题与回答](https://elitedatascience.com/mlqa-reading-list)
## 我崇拜的公司
- [ ] [ELSA - 你虚拟的口语教练](https://www.elsanow.io/home)
================================================
FILE: README-zh-TW.md
================================================
# 自上而下的學習路線：軟體工程師的機器學習
靈感來源於[谷歌面試學習手冊](https://github.com/jwasham/google-interview-university/blob/master/README-cn.md)
> * 原文地址：[軟體工程師的機器學習](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
> * 原文作者：[ZuzooVn(Nam Vu)](https://github.com/ZuzooVn)
> * 翻譯：[NeroCube](https://github.com/NeroCube)
## 這是？
這是本人為期數月的學習計劃。我正要從一名行動裝置開發者(自學，沒有計算機學位)轉型成為一名機器學習工程師。
我的主要目標是找到一種以實踐為主的學習方法，並為初學者抽象掉大多數的數學概念。
這種學習方法是非傳統的，因為它是專門為軟體工程師所設計的自上而下，以結果為導向的學習方法。
如果您想讓它更好的話，隨時歡迎您的貢獻。
---
## 目錄
- [這是？](#這是)
- [為何要用到它？](#為何要用到它)
- [如何使用它？](#如何使用它)
- [Follow me](#follow-me)
- [別認為自己不夠聰明](#別認為自己不夠聰明)
- [關於影片資源](#關於影片資源)
- [預備知識](#預備知識)
- [每日計劃](#每日計劃)
- [動機](#動機)
- [機器學習概論](#機器學習概論)
- [掌握機器學習](#掌握機器學習)
- [有趣的機器學習](#有趣的機器學習)
- [染墨的機器學習簡介](#染墨的機器學習簡介)
- [一本深入的機器學習指南](#一本深入的機器學習指南)
- [故事與經驗](#故事與經驗)
- [機器學習演算法](#機器學習演算法)
- [入門書籍](#入門書籍)
- [實用書籍](#實用書籍)
- [Kaggle知識競賽](#kaggle知識競賽)
- [系列影片](#系列影片)
- [MOOC](#mooc)
- [資源](#資源)
- [遊戲](#遊戲)
- [成為一名開源貢獻者](#成為一名開源貢獻者)
- [廣播](#廣播)
- [社區](#社區)
- [相關會議](#相關會議)
- [面試問題](#面試問題)
- [我崇拜的公司](#我崇拜的公司)
---
## 為何要用到它？
我會為了我未來的工作————機器學習工程師 遵循這份計劃。自2011年以來，我一直進行著行動裝置的開發(包括安卓、iOS 與黑莓)。我有軟體工程的文憑，但沒有計算機科學的文憑。我僅僅在大學的時候學習過一點基礎科學，包括微積分、線性代數、離散數學、概率論與統計。
我認真思考過我在機器學習方面的興趣:
- [我能在沒有計算機科學碩士、博士文憑的情況下找到一份關於機器學習的工作嗎？](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD)
- *"你當然可以，但是我想進入這個領域則無比艱難。"* [Drac Smith](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD/answer/Drac-Smith?srid=oT0p)
- [我是一名自學機器學習的軟體工程師，我如何在沒有相關經驗的情況下找到一份關於機器學習的工作？](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work)
- *"我正在為我的團隊招聘機器學習專家，但你的 MOOC 並不會給你帶來工作機會。事實上，大多數機器學習方向的碩士也並不會得到工作機會，因為他們(與大多數上過 MOOC 的人一樣)並沒有深入地去理解。他們都沒法幫助我的團隊解決問題。"* [Ross C. Taylor](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work/answer/Ross-C-Taylor?srid=oT0p)
- [找一份機器學習相關的工作需要掌握怎樣的技能？](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs)
- *"首先，你得有正兒八經的計科或數學專業背景。ML 是一個比較先進的課題，大多數的教材都會直接默認你有以上背景。其次，機器學習是一個集成了許多子專業的奇技淫巧的課題，你甚至會想看看 MS 的機器學習課程，去看看他們的授課、課程和教材。"* [Uri](http://softwareengineering.stackexchange.com/a/79717)
- *"統計，假設，分佈式計算，然後繼續統計。"* [Hydrangea](http://softwareengineering.stackexchange.com/a/79575)
我發現自己遇到了麻煩。
據我所知, [機器學習有兩個方向](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/)：
- 實用機器學習： 這個方向主要是查詢資料庫、數據清洗、寫腳本來轉換數據，把演算法和函式庫結合起來再加上一些客製化的程式，從數據中擠出一些準確的答案來證明一些困難且模糊不清的問題。實際上它非常混亂。
- 理論機器學習： 這個方向主要是關於數學、抽象、理想狀況、極限條件、典型例子以及一切可能的特徵。這個方向十分的乾凈、整潔，遠離混亂的現實。
我認為對於以實踐為主的人來說，做好的方法就是 [“練習--學習--練習”](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985)，這意味著每個學生一開始就能參與一些現有項目與一些問題，並練習(解決)它們以熟悉傳統的方法是怎麼做的。在有了一些簡單的練習經驗之後，他們就可以開始鑽進書里去學習理論知識。這些理論知識將幫助他們在將來進行更進一步的訓練，充實他們解決實際問題的工具箱。學習理論知識還會加深他們對那些簡單練習的理解，幫助他們更快地獲得進階的經驗。
這是一個很長的計劃，它花去了我一年的時間。如果你已經對它有所瞭解了，它將會讓你省去很多時間。
## 如何使用它？
以下的內容全部是概要，你需要由上往下來解決這些項目。
我使用 Github 獨特的 flavored markdown 方式，以任務列表來檢查我計劃的進展。
- [x] 創建一個新的分支，然後你可以這樣來標出你已經完成的項目，只需要在框中填寫一個x即可：[x]
[瞭解更多有關 Github-flavored markdown 的知識](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Follow me
我是一名非常非常想去美國工作的越南軟體工程師。
我在這份計劃中花多少時間？在每天的艱辛工作完成後，每晚花4小時。
我已經在實現夢想的旅途中了。
- Twitter: [@Nam Vu](https://twitter.com/zuzoovn)
| |
|:---:|
| USA as heck |
## 別認為自己不夠聰明
當我打開書本，發現他們告訴我多變量微積分、推理統計、線性代數是學習機器學習的先決條件的時候，我非常沮喪。因為我不知道從哪兒開始…
- [我數學不好怎麼辦](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [沒有數學專業背景而理解機器學習演算法的5種技巧](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/)
- [我是如何學習機器學習的？](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
## 關於影片資源
部分影音只有在 Coursera 、 EdX 的課程註冊了才能觀看。雖然它們是免費的，但有些時間段這些課程並不開放，你可能需要等上一段時間(可能是好幾個月)。我將會加上更多的公開的影片源來代替這些在線課程的影片。我很喜歡大學的講座。
## 預備知識
這個小章節是一些在每日計劃開始前我想去瞭解的一些預備知識與一些有趣的信息。
- [ ] [資料分析，資料解析，資料探勘，資料科學，機器學習，大數據的區別是什麼？](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1)
- [ ] [學習如何去學習](https://www.coursera.org/learn/learning-how-to-learn)
- [ ] [不要斬斷鎖鏈](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [ ] [如何自學](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## 每日計劃
每個主題都不需要用一整天來完全理解它們，你可以每天完成多個。
每天我都會從下面的列表中選一個出來，一遍又一遍的讀，做筆記，練習，用 Python 或 R語言實現它。
# 動機
- [ ] [夢](https://www.youtube.com/watch?v=g-jwWYX7Jlo)
## 機器學習概論
- [ ] [機器學習的形象簡介](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [一份輕鬆的機器學習指南](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)
- [ ] [為開發者準備的機器學習簡介](http://blog.algorithmia.com/introduction-machine-learning-developers/)
- [ ] [菜鳥的機器學習基礎](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
- [ ] [你如何向非計算機專業的人來解釋機器學習與資料探勘？](https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people)
- [ ] [機器學習：葫蘆裡賣什麼藥，部落格簡單明瞭地介紹了機器學習的原理](https://georgemdallas.wordpress.com/2013/06/11/big-data-data-mining-and-machine-learning-under-the-hood/)
- [ ] [機器學習是什麼？它是如何工作的呢？](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1)
- [ ] [深度學習——一份非技術性的簡介](http://www.slideshare.net/AlfredPong1/deep-learning-a-nontechnical-introduction-69385936)
## 掌握機器學習
- [ ] [掌握機器學習的方法](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [程式設計師的機器學習](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [掌握並運用機器學習](http://machinelearningmastery.com/start-here/)
- [ ] [Python 機器學習小課程](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [機器學習演算法小課程](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## 有趣的機器學習
- [ ] [機器學習真有趣！](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Part 2: 使用機器學習來創造超級馬利奧的關卡](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [Part 3: 深度學習與捲積神經網路](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637)
- [ ] [Part 4: 現代人臉識別與深度學習](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc)
- [ ] [Part 5: 翻譯與深度學習和序列的魔力](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c)
- [ ] [Part 6: 如何使用深度學習進行語音識別](https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a#.lhr1nnpcy)
- [ ] [Part 7: 使用生成式對抗網路創造 8 像素藝術](https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7)
- [ ] [Part 8: 如何故意欺騙神經網路](https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196)
## [染墨的機器學習簡介](https://triskell.github.io/2016/11/15/Inky-Machine-Learning.html)
- [ ] [Part 1 : 什麼是機器學習？](https://triskell.github.io/2016/10/23/What-is-Machine-Learning.html)
- [ ] [Part 2 : 監督學習與非監督學習](https://triskell.github.io/2016/11/13/Supervised-Learning-and-Unsupervised-Learning.html)
## 一本深入的機器學習指南
- [ ] [概述，目標，學習類型和演算法](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [數據的選擇，準備與建模](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [模型的評估，驗證，復雜性與改進](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [模型性能與誤差分析](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [無監督學習，相關領域與實踐中的機器學習](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## 故事與經驗
- [ ] [一周的機器學習](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [一年的機器學習](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [我是如何在3天內寫出我的第一個機器學習程式的](http://blog.adnansiddiqi.me/how-i-wrote-my-first-machine-learning-program-in-3-days/)
- [ ] [學習路徑：你成為機器學習專家的導師](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [不是 PhD 你也可以成為機器學習的明日之星](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7)
- [ ] 如何6個月成為一名資料科學家：一名駭客的職業規劃
- [影片](https://www.youtube.com/watch?v=rIofV14c0tc)
- [投影片](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [5個你成為機器學習工程師必須要掌握的技能](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html)
- [ ] [你是一個自學成才的機器學習工程師嗎？你是怎麼做的？花了多長時間？](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you)
- [ ] [一個人如何成為一名優秀的機器學習工程師？](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
- [ ] [一個專注於機器學習的學術假期](http://karlrosaen.com/ml/)
## 機器學習演算法
- [ ] [用10種機器學習演算法來解釋“部隊士兵”](https://www.analyticsvidhya.com/blog/2015/12/10-machine-learning-algorithms-explained-army-soldier/)
- [ ] [Top10的資料探勘演算法](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/)
- [ ] [介紹10種機器學習的術語](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/)
- [ ] [機器學習演算法之旅](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
- [ ] [機器學習工程師需要知道的10種演算法](https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.ofc7t2965)
- [ ] [比較監督學習演算法](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
- [ ] [機器學習算法：最簡化、可執行的機器學習演算法集合](https://github.com/rushter/MLAlgorithms)
## 入門書籍
- [ ] [《Data Smart: Using Data Science to Transform Information into Insight》第 1 版](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [《Data Science for Business: What you need to know about data mining and data analytic-thinking》](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)
- [ ] [《Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die》](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## 實用書籍
- [ ] [Hacker 的機器學習](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository(R)](https://github.com/johnmyleswhite/ML_for_Hackers)
- [GitHub repository(Python)](https://github.com/carljv/Will_it_Python)
- [ ] [Python 機器學習](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [集體智能編程: 創建智慧 Web 2.0 應用](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [機器學習: 演算法視角，第二版](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Python 機器學習簡介: 資料科學家指南](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [資料探勘: 機器學習工具與技術實踐，第 3 版](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Teaching material
- [1-5 章投影片(zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [6-8 章投影片(zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [Reactive Machine Learning Systems(MEAP)](https://www.manning.com/books/reactive-machine-learning-systems)
- [GitHub repository](https://github.com/jeffreyksmithjr/reactive-machine-learning-systems)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
- [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html)
- [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python)
- [影片](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
- [ ] [使用 Python 構建機器學習系統](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python)
- [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)
- [ ] [學習 scikit-learn: 用 Python 進行機器學習](https://www.packtpub.com/big-data-and-business-intelligence/learning-scikit-learn-machine-learning-python)
- [GitHub repository](https://github.com/gmonce/scikit-learn-book)
- [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
- [ ] [Probabilistic Graphical Models: Principles and Techniques](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193)
- [ ] [Machine Learning: Hands-On for Developers and Technical Professionals](https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061)
- [Machine Learning Hands-On for Developers and Technical Professionals review](https://blogs.msdn.microsoft.com/querysimon/2015/01/01/book-review-machine-learning-hands-on-for-developers-and-technical-professionals/)
- [GitHub repository](https://github.com/jasebell/mlbook)
- [ ] [從數據中學習](https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069)
- [在線教學](https://work.caltech.edu/telecourse.html)
- [ ] [強化學習——簡介(第 2 版)](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)
- [GitHub repository](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
- [ ] [使用TensorFlow(MEAP)進行機器學習](https://www.manning.com/books/machine-learning-with-tensorflow)
- [GitHub repository](https://github.com/BinRoot/TensorFlow-Book)
## Kaggle知識競賽
- [ ] [Kaggle 競賽：怎麼樣，在哪裡開始？](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [一個初學者如何用一個小項目在機器學習入門並在 Kaggle 競爭](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle)
- [ ] [如何競爭 Kaggle 的 Master](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## 系列影片
- [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Josh Gordon 的機器學習菜譜](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [在 30 分鐘以內瞭解機器學習的一切](https://vimeo.com/43547079)
- [ ] [一份友好的機器學習簡介](https://www.youtube.com/watch?v=IpGxLWOIZy4)
- [ ] [Nuts and Bolts of Applying Deep Learning - Andrew Ng](https://www.youtube.com/watch?v=F1ka6a13S9I)
- [ ] BigML Webinar
- [影片](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo)
- [資源](https://bigml.com/releases)
- [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA)
- [ ] [Machine learning in Python with scikit-learn](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A)
- [GitHub repository](https://github.com/justmarkham/scikit-learn-videos)
- [部落格](http://blog.kaggle.com/author/kevin-markham/)
- [ ] [播放清單 - YouTuBe 上最熱門的機器學習、神經網路、深度學習影片](https://www.analyticsvidhya.com/blog/2015/07/top-youtube-videos-machine-learning-neural-network-deep-learning/)
- [ ] [16 個必看的機器學習教學](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/)
- [ ] [DeepLearning.TV](https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ)
- [ ] [Learning To See](https://www.youtube.com/playlist?list=PLiaHhY2iBX9ihLasvE8BKnS2Xg8AhY6iV)
- [ ] [神經網路課程 - Université de Sherbrooke](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
- [ ] [2016年 的 21 個深度學習影片課程](https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/)
- [ ] [2016 年的 30 個頂級的機器學習與人工智慧影片教學 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016](https://www.analyticsvidhya.com/blog/2016/12/30-top-videos-tutorials-courses-on-machine-learning-artificial-intelligence-from-2016/)
- [ ] [程式設計者的深度學習實戰](http://course.fast.ai/index.html)
## MOOC
- [ ] [edX 的人工智慧導論](https://www.edx.org/course/introduction-artificial-intelligence-ai-microsoft-dat263x)
- [ ] [Udacity 的機器學習導論](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [復習 Udacity 機器學習導論](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Udacity 的監督學習、非監督學習及深入](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)
- [ ] [Coursera的機器學習](https://www.coursera.org/learn/machine-learning)
- [影片](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [復習 Coursera 機器學習](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera 的機器學習路線圖](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
- [ ] [機器學習淬煉](https://code.tutsplus.com/courses/machine-learning-distilled)
- [ ] [BigML training](https://bigml.com/training)
- [ ] [Coursera 的神經網路課程](https://www.coursera.org/learn/neural-networks)
- 由Geoffrey Hinton(神經網路的先驅)執教
- [ ] [使用 TensorFlow 創建深度學習應用](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info)
- [ ] [描述統計學概論](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
- [ ] [推理統計學概論](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)
- [ ] [6.S094: 自動駕駛的深度學習](http://selfdrivingcars.mit.edu/)
- [ ] [6.S191: 深度學習簡介](http://introtodeeplearning.com/index.html)
- [ ] [Coursera 深度學習教學](https://www.coursera.org/specializations/deep-learning)
## 資源
- [ ] [一個月學會機器學習](https://elitedatascience.com/machine-learning-masterclass)
- [ ] [一份“非技術性”的機器學習與人工智慧指南](https://medium.com/@samdebrule/a-humans-guide-to-machine-learning-e179f43b67a0#.cpzf3a5c0)
- [ ] [Google 機器學習工程師最佳實踐教學](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [ ] [Hacker News 的《軟體工程師的機器學習》](https://news.ycombinator.com/item?id=12898718)
- [ ] [開發者的機器學習](https://xyclade.github.io/MachineLearning/)
- [ ] [為人類🤖👶準備的機器學習](https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12)
- [ ] [給開發者的關於機器學習的建議](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [機器學習入門](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [為新手準備的機器學習入門教學](https://medium.com/@suffiyanz/getting-started-with-machine-learning-f15df1c283ea#.yjtiy7ei9)
- [ ] [初學者如何自學機器學習](https://elitedatascience.com/learn-machine-learning)
- [ ] [機器學習自學資源](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [提升你的機器學習技能](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] [一份'真誠'的機器學習指南](https://medium.com/axiomzenteam/an-honest-guide-to-machine-learning-2f6d7a6df60e#.ib12a1yw5)
- [ ] 用機器學習讓 Hacker News 更具可讀性
- [影片](https://www.youtube.com/watch?v=O7IezJT9uSI)
- [投影片](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely)
- [ ] [深入機器學習](https://github.com/hangtwenty/dive-into-machine-learning)
- [ ] [軟體工程師的{機器、深度}學習](https://speakerdeck.com/pmigdal/machine-deep-learning-for-software-engineers)
- [ ] [深度學習入門](https://deeplearning4j.org/deeplearningforbeginners.html)
- [ ] [深度學習基礎](https://github.com/pauli-space/foundations_for_deep_learning)
- [ ] [機器學習思維導圖/小抄](https://github.com/dformoso/machine-learning-mindmap)
- 大學中的機器學習課程
- [ ] [斯坦福](http://ai.stanford.edu/courses/)
- [ ] [機器學習夏令營](http://mlss.cc/)
- [ ] [牛津](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [劍橋](http://mlg.eng.cam.ac.uk/)
- Flipboard的主題
- [機器學習](https://flipboard.com/topic/machinelearning)
- [深度學習](https://flipboard.com/topic/deeplearning)
- [人工智慧](https://flipboard.com/topic/artificialintelligence)
- Medium的主題
- [機器學習](https://medium.com/tag/machine-learning/latest)
- [深度學習](https://medium.com/tag/deep-learning)
- [人工智慧](https://medium.com/tag/artificial-intelligence)
- 每月文章Top10
- 機器學習
- [2016年7月](https://medium.mybridge.co/top-ten-machine-learning-articles-for-the-past-month-9c1202351144#.lyycen64y)
- [2016年8月](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-2f3cb815ffed#.i9ee7qkjz)
- [2016年9月](https://medium.mybridge.co/machine-learning-top-10-in-september-6838169e9ee7#.4jbjcibft)
- [2016年10月](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-35c37825a943#.td5im1p5z)
- [2016年11月](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-b499e4213a34#.7k39i08tv)
- [2016年](https://medium.mybridge.co/machine-learning-top-10-of-the-year-v-2017-7552599935c0#.wtx2mchqn)
- 演算法
- [2016年9月](https://medium.mybridge.co/algorithm-top-10-articles-in-september-8a0e6afb0807#.hgjzuyxdb)
- [2016年10月-11月](https://medium.mybridge.co/algorithm-top-10-articles-v-november-e73cba2fa87e#.kothimkhb)
- [全面的數據科學資源清單](http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources)
- [DigitalMind 的人工智慧資源](http://blog.digitalmind.io/post/artificial-intelligence-resources)
- [令人驚嘆的機器學習](https://github.com/josephmisiti/awesome-machine-learning)
- [CreativeAi 的機器學習](http://www.creativeai.net/?cat%5B0%5D=machine-learning)
## 遊戲
- [Halite：AI 編程遊戲](https://halite.io/)
- [Vindinium: 挑戰 AI 編程](http://vindinium.org/)
- [Video Game AI 比賽](http://www.gvgai.net/)
- [憤怒的小鳥 AI 比賽](https://aibirds.org/)
- [The AI Games](http://theaigames.com/)
- [Fighting Game AI Competition](http://www.ice.ci.ritsumei.ac.jp/~ftgaic/)
- [CodeCup](http://www.codecup.nl/intro.php)
- [星際爭霸 AI 學生錦標賽](http://sscaitournament.com/)
- [AIIDE 星際爭霸 AI 競賽](http://www.cs.mun.ca/~dchurchill/starcraftaicomp/)
- [CIG 星際爭霸 AI 競賽](https://sites.google.com/site/starcraftaic/)
- [CodinGame - AI Bot Games](https://www.codingame.com/training/machine-learning)
## 成為一名開源貢獻者
- [ ] [tensorflow/magenta: Magenta: 用機器智慧生成音樂與藝術](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: 使用數據流圖進行計算進行可擴展的機器學習](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: 使用深層神經網路進行面部識別](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: 神經網路模型語法](https://github.com/tensorflow/models/tree/master/syntaxnet)
## 廣播
- ### 適合初學者的廣播：
- [Talking Machines](http://www.thetalkingmachines.com/)
- [Linear Digressions](http://lineardigressions.com/)
- [Data Skeptic](http://dataskeptic.com/)
- [This Week in Machine Learning & AI](https://twimlai.com/)
- ### “更多”進階的廣播：
- [Partially Derivative](http://partiallyderivative.com/)
- [O’Reilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast)
- [Not So Standard Deviation](https://soundcloud.com/nssd-podcast)
- ### 盒子外的廣播：
- [Data Stories](http://datastori.es/)
## 社區
- Quora
- [機器學習](https://www.quora.com/topic/Machine-Learning)
- [統計學](https://www.quora.com/topic/Statistics-academic-discipline)
- [資料探勘](https://www.quora.com/topic/Data-Mining)
- Reddit
- [機器學習](https://www.reddit.com/r/machinelearning)
- [計算機視覺](https://www.reddit.com/r/computervision)
- [自然語言處理](https://www.reddit.com/r/languagetechnology)
- [資料科學](https://www.reddit.com/r/datascience)
- [大數據](https://www.reddit.com/r/bigdata)
- [統計學](https://www.reddit.com/r/statistics)
- [Data Tau](http://www.datatau.com/)
- [Deep Learning News](http://news.startup.ml/)
- [KDnuggets](http://www.kdnuggets.com/)
## 相關會議
- ([NIPS](https://nips.cc/))
- ([ICLR](http://www.iclr.cc/doku.php?id=ICLR2017:main&redirect=1))
- ([AAAI](http://www.aaai.org/Conferences/AAAI/aaai17.php))
- ([IEEE CIG](http://www.ieee-cig.org/))
- ([IEEE ICMLA](http://www.icmla-conference.org/))
- ([ICML](https://2017.icml.cc/))
## 面試問題
- [ ] [如何準備機器學習職位的面試](http://blog.udacity.com/2016/05/prepare-machine-learning-interview.html)
- [ ] [40 個機器學習與資料科學的面試問題](https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science)
- [ ] [21 個必須要知道的資料科學問題與回答](http://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html)
- [ ] [Top 50 機器學習面試問題與回答](http://career.guru99.com/top-50-interview-questions-on-machine-learning/)
- [ ] [機器學習面試問題](https://resources.workable.com/machine-learning-engineer-interview-questions)
- [ ] [常用的機器學習面試問題](http://www.learn4master.com/machine-learning/popular-machine-learning-interview-questions)
- [ ] [機器學習面試問題有哪些相同的？](https://www.quora.com/What-are-some-common-Machine-Learning-interview-questions)
- [ ] [什麼是評價一個機器學習研究者的最好的問題？](https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher)
- [ ] [機器學習面試問題大搜集](http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/)
- [ ] [121 個需要掌握的問題與回答](https://elitedatascience.com/mlqa-reading-list)
## 我崇拜的公司
- [ ] [ELSA - 你虛擬的口語教練](https://www.elsanow.io/home)
================================================
FILE: README.md
================================================
# Top-down learning path: Machine Learning for Software Engineers
<p align="center">
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers">
<img alt="Top-down learning path: Machine Learning for Software Engineers" src="https://img.shields.io/badge/Machine%20Learning-Software%20Engineers-blue.svg">
</a>
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/ZuzooVn/machine-learning-for-software-engineers.svg">
</a>
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers/network">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/ZuzooVn/machine-learning-for-software-engineers.svg">
</a>
</p>
Inspired by [Coding Interview University](https://github.com/jwasham/coding-interview-university).
Translations: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-pt-BR.md) | [中文版本](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-zh-CN.md) | [Français](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-fr-FR.md) | [臺灣華語版本](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-zh-TW.md)
[How I (Nam Vu) plan to become a machine learning engineer](https://www.codementor.io/zuzoovn/how-i-plan-to-become-a-machine-learning-engineer-a4metbcuk)
## What is it?
This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.
My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the Math for the beginner.
This approach is unconventional because it’s the top-down and results-first approach designed for software engineers.
Please, feel free to make any contributions you feel will make it better.
---
## Table of Contents
- [What is it?](#what-is-it)
- [Why use it?](#why-use-it)
- [How to use it](#how-to-use-it)
- [Follow me](#follow-me)
- [Don't feel you aren't smart enough](#dont-feel-you-arent-smart-enough)
- [About Video Resources](#about-video-resources)
- [Prerequisite Knowledge](#prerequisite-knowledge)
- [The Daily Plan](#the-daily-plan)
- [Motivation](#motivation)
- [Machine learning overview](#machine-learning-overview)
- [Machine learning mastery](#machine-learning-mastery)
- [Machine learning is fun](#machine-learning-is-fun)
- [Inky Machine Learning](#inky-machine-learning)
- [Machine Learning: An In-Depth Guide](#machine-learning-an-in-depth-guide)
- [Stories and experiences](#stories-and-experiences)
- [Machine Learning Algorithms](#machine-learning-algorithms)
- [Beginner Books](#beginner-books)
- [Practical Books](#practical-books)
- [Kaggle knowledge competitions](#kaggle-knowledge-competitions)
- [Video Series](#video-series)
- [MOOC](#mooc)
- [Resources](#resources)
- [Becoming an Open Source Contributor](#becoming-an-open-source-contributor)
- [Games](#games)
- [Podcasts](#podcasts)
- [Communities](#communities)
- [Conferences](#conferences)
- [Interview Questions](#interview-questions)
- [My admired companies](#my-admired-companies)
---
## Why use it?
I'm following this plan to prepare for my near-future job: Machine learning engineer. I've been building native mobile applications (Android/iOS/Blackberry) since 2011. I have a Software Engineering degree, not a Computer Science degree. I have an itty-bitty amount of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics from university.
Think about my interest in machine learning:
- [Can I learn and get a job in Machine Learning without studying CS Master and PhD?](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD)
- *"You can, but it is far more difficult than when I got into the field."* [Drac Smith](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD/answer/Drac-Smith?srid=oT0p)
- [How do I get a job in Machine Learning as a software programmer who self-studies Machine Learning, but never has a chance to use it at work?](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work)
- *"I'm hiring machine learning experts for my team and your MOOC will not get you the job (there is better news below). In fact, many people with a master's in machine learning will not get the job because they (and most who have taken MOOCs) do not have a deep understanding that will help me solve my problems."* [Ross C. Taylor](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work/answer/Ross-C-Taylor?srid=oT0p)
- [What skills are needed for machine learning jobs?](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs)
- *"First, you need to have a decent CS/Math background. ML is an advanced topic so most textbooks assume that you have that background. Second, machine learning is a very general topic with many sub-specialties requiring unique skills. You may want to browse the curriculum of an MS program in Machine Learning to see the course, curriculum and textbook."* [Uri](http://softwareengineering.stackexchange.com/a/79717)
- *"Probability, distributed computing, and Statistics."* [Hydrangea](http://softwareengineering.stackexchange.com/a/79575)
I find myself in times of trouble.
AFAIK, [There are two sides to machine learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/):
- Practical Machine Learning: This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill-defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
I think the best way for practice-focused methodology is something like ['practice — learning — practice'](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985), that means where students first come with some existing projects with problems and solutions (practice) to get familiar with traditional methods in the area and perhaps also with their methodology. After practicing with some elementary experiences, they can go into the books and study the underlying theory, which serves to guide their future advanced practice and will enhance their toolbox of solving practical problems. Studying theory also further improves their understanding on the elementary experiences, and will help them acquire advanced experiences more quickly.
It's a long plan. It's going to take me years. If you are familiar with a lot of this already it will take you a lot less time.
## How to use it
Everything below is an outline, and you should tackle the items in order from top to bottom.
I'm using Github's special markdown flavor, including tasks lists to check progress.
- [x] Create a new branch so you can check items like this, just put an x in the brackets: [x]
[More about Github-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Follow me
I'm a Vietnamese Software Engineer who is really passionate and wants to work in the USA.
How much did I work during this plan? Roughly 4 hours/night after a long, hard day at work.
I'm on the journey.
- Twitter: [@Nam Vu](https://twitter.com/zuzoovn)
| |
|:---:|
| USA as heck |
## Don't feel you aren't smart enough
I get discouraged from books and courses that tell me as soon as I open them that multivariate calculus, inferential statistics and linear algebra are prerequisites. I still don’t know how to get started…
- [What if I’m Not Good at Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/)
- [How do I learn machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
## About Video Resources
Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes
are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos
from public sources and replacing the online course videos over time. I like using university lectures.
## Prerequisite Knowledge
This short section consists of prerequisites/interesting info I wanted to learn before getting started on the daily plan.
- [ ] [What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1)
- [ ] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
- [ ] [Don’t Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [ ] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## The Daily Plan
Each subject does not require a whole day to be able to understand it fully, and you can do multiple of these in a day.
Each day I take one subject from the list below, read it cover to cover, take notes, do the exercises and write an implementation in Python or R.
# Motivation
- [ ] [Dream](https://www.youtube.com/watch?v=g-jwWYX7Jlo)
## Machine learning overview
- [ ] [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [Gentle Guide to Machine Learning](https://blog.monkeylearn.com/gentle-guide-to-machine-learning/)
- [ ] [Introduction to Machine Learning for Developers](http://blog.algorithmia.com/introduction-machine-learning-developers/)
- [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
- [ ] [How do you explain Machine Learning and Data Mining to non Computer Science people?](https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people)
- [ ] [Machine Learning: Under the hood. Blog post explains the principles of machine learning in layman terms. Simple and clear](https://georgemdallas.wordpress.com/2013/06/11/big-data-data-mining-and-machine-learning-under-the-hood/)
- [ ] [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1)
- [ ] [How to Become a Machine Learning Engineer?](https://www.scaler.com/blog/how-to-become-a-machine-learning-engineer/)
- ~~[] [Deep Learning - A Non-Technical Introduction](http://www.slideshare.net/AlfredPong1/deep-learning-a-nontechnical-introduction-69385936)~~[removed]
## Machine learning mastery
- [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [Machine Learning for Programmers](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [Applied Machine Learning with Machine Learning Mastery](http://machinelearningmastery.com/start-here/)
- [ ] [Python Machine Learning Mini-Course](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [Machine Learning Algorithms Mini-Course](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## Machine learning is fun
- [ ] [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Part 2: Using Machine Learning to generate Super Mario Maker levels](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [Part 3: Deep Learning and Convolutional Neural Networks](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637)
- [ ] [Part 4: Modern Face Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc)
- [ ] [Part 5: Language Translation with Deep Learning and the Magic of Sequences](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c)
- [ ] [Part 6: How to do Speech Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a#.lhr1nnpcy)
- [ ] [Part 7: Abusing Generative Adversarial Networks to Make 8-bit Pixel Art](https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7)
- [ ] [Part 8: How to Intentionally Trick Neural Networks](https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196)
## [Inky Machine Learning](https://triskell.github.io/2016/11/15/Inky-Machine-Learning.html)
- [ ] [Part 1: What is Machine Learning ?](https://triskell.github.io/2016/10/23/What-is-Machine-Learning.html)
- [ ] [Part 2: Supervised Learning and Unsupervised Learning](https://triskell.github.io/2016/11/13/Supervised-Learning-and-Unsupervised-Learning.html)
## Machine Learning: An In-Depth Guide
- [ ] [Overview, goals, learning types, and algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [Data selection, preparation, and modeling](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [Model evaluation, validation, complexity, and improvement](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [Model performance and error analysis](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [Unsupervised learning, related fields, and machine learning in practice](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## Stories and experiences
- [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [How I wrote my first Machine Learning program in 3 days](http://blog.adnansiddiqi.me/how-i-wrote-my-first-machine-learning-program-in-3-days/)
- [ ] [Learning Path : Your mentor to become a machine learning expert](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [You Too Can Become a Machine Learning Rock Star! No PhD](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7)
- [ ] How to become a Data Scientist in 6 months: A hacker’s approach to career planning
- [Video](https://www.youtube.com/watch?v=rIofV14c0tc)
- [Slide](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [5 Skills You Need to Become a Machine Learning Engineer](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html)
- [ ] [Are you a self-taught machine learning engineer? If yes, how did you do it & how long did it take you?](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you)
- [ ] [How can one become a good machine learning engineer?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
- [ ] [A Learning Sabbatical focused on Machine Learning](http://karlrosaen.com/ml/)
## Machine Learning Algorithms
- [ ] [10 Machine Learning Algorithms Explained to an ‘Army Soldier’](https://www.analyticsvidhya.com/blog/2015/12/10-machine-learning-algorithms-explained-army-soldier/)
- [ ] [Top 10 data mining algorithms in plain English](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/)
- [ ] [10 Machine Learning Terms Explained in Simple English](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/)
- [ ] [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
- [ ] [The 10 Algorithms Machine Learning Engineers Need to Know](https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.ofc7t2965)
- [ ] [Comparing supervised learning algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
- [ ] [Machine Learning Algorithms: A collection of minimal and clean implementations of machine learning algorithms](https://github.com/rushter/MLAlgorithms)
- [ ] [KNN Algorithm in Machine Learning](https://www.scaler.com/topics/what-is-knn-algorithm-in-machine-learning/)
## Beginner Books
- [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [Data Science for Business: What you need to know about data mining and data analytic-thinking](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)
- [ ] [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## Practical Books
- [ ] [Machine Learning for Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository(R)](https://github.com/johnmyleswhite/ML_for_Hackers)
- [GitHub repository(Python)](https://github.com/carljv/Will_it_Python)
- [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Introduction to Machine Learning with Python: A Guide for Data Scientists](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Teaching material
- [Slides for Chapters 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [Slides for Chapters 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [Reactive Machine Learning Systems(MEAP)](https://www.manning.com/books/reactive-machine-learning-systems)
- [GitHub repository](https://github.com/jeffreyksmithjr/reactive-machine-learning-systems)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
- [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html)
- [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python)
- [Videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
- [ ] [Building Machine Learning Systems with Python](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python)
- [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)
- [ ] [Learning scikit-learn: Machine Learning in Python](https://www.packtpub.com/big-data-and-business-intelligence/learning-scikit-learn-machine-learning-python)
- [GitHub repository](https://github.com/gmonce/scikit-learn-book)
- [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
- [ ] [Probabilistic Graphical Models: Principles and Techniques](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193)
- [ ] [Machine Learning: Hands-On for Developers and Technical Professionals](https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061)
- [Machine Learning Hands-On for Developers and Technical Professionals review](https://blogs.msdn.microsoft.com/querysimon/2015/01/01/book-review-machine-learning-hands-on-for-developers-and-technical-professionals/)
- [GitHub repository](https://github.com/jasebell/mlbook)
- [ ] [Learning from Data](https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069)
- [Online tutorials](https://work.caltech.edu/telecourse.html)
- [ ] [Reinforcement Learning: An Introduction (2nd Edition)](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)
- [GitHub repository](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
- [ ] [Machine Learning with TensorFlow(MEAP)](https://www.manning.com/books/machine-learning-with-tensorflow)
- [GitHub repository](https://github.com/BinRoot/TensorFlow-Book)
- [ ] [How Machine Learning Works (MEAP)](https://www.manning.com/books/how-machine-learning-works)
- [GitHub repository](https://github.com/Mostafa-Samir/How-Machine-Learning-Works)
- [ ] [Succeeding with AI](https://www.manning.com/books/succeeding-with-ai)
## Kaggle knowledge competitions
- [ ] [Kaggle Competitions: How and where to begin?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [How a Beginner Used Small Projects To Get Started in Machine Learning and Compete on Kaggle](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle)
- [ ] [Master Kaggle By Competing Consistently](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## Video Series
- [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [Everything You Need to know about Machine Learning in 30 Minutes or Less](https://vimeo.com/43547079)
- [ ] [A Friendly Introduction to Machine Learning](https://www.youtube.com/watch?v=IpGxLWOIZy4)
- [ ] [Nuts and Bolts of Applying Deep Learning - Andrew Ng](https://www.youtube.com/watch?v=F1ka6a13S9I)
- [ ] BigML Webinar
- [Video](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo)
- [Resources](https://bigml.com/releases)
- [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA)
- [ ] [Machine learning in Python with scikit-learn](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A)
- [GitHub repository](https://github.com/justmarkham/scikit-learn-videos)
- [Blog](http://blog.kaggle.com/author/kevin-markham/)
- [ ] [My playlist – Top YouTube Videos on Machine Learning, Neural Network & Deep Learning](https://www.analyticsvidhya.com/blog/2015/07/top-youtube-videos-machine-learning-neural-network-deep-learning/)
- [ ] [16 New Must Watch Tutorials, Courses on Machine Learning](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/)
- [ ] [DeepLearning.TV](https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ)
- [ ] [Learning To See](https://www.youtube.com/playlist?list=PLiaHhY2iBX9ihLasvE8BKnS2Xg8AhY6iV)
- [ ] [Neural networks class - Université de Sherbrooke](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
- [ ] [21 Deep Learning Videos, Tutorials & Courses on Youtube from 2016](https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/)
- [ ] [30 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016](https://www.analyticsvidhya.com/blog/2016/12/30-top-videos-tutorials-courses-on-machine-learning-artificial-intelligence-from-2016/)
- [ ] [Practical Deep Learning For Coders](http://course.fast.ai/index.html)
- [ ] [Practical Deep Learning For Coders Version 2 (PyTorch)](http://forums.fast.ai/t/welcome-to-part-1-v2/5787)
## MOOC
- [ ] [Coursera’s AI For Everyone](https://www.coursera.org/learn/ai-for-everyone)
- [ ] [edX's Introduction to Artificial Intelligence (AI)](https://www.edx.org/course/introduction-artificial-intelligence-ai-microsoft-dat263x)
- [ ] [Udacity’s Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [Udacity Intro to Machine Learning Review](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Udacity’s Supervised, Unsupervised & Reinforcement](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)
- [ ] [Machine Learning & AI Foundations: Value Estimations](https://www.lynda.com/Data-Science-tutorials/Machine-Learning-Essential-Training-Value-Estimations/548594-2.html)
- [ ] [Kaggle's Hands-On Data Science Education](https://www.kaggle.com/learn/overview)
- [ ] [Microsoft Professional Program for Artificial Intelligence](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/)
- [ ] [Coursera’s Machine Learning](https://www.coursera.org/learn/machine-learning)
- [Video only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [Coursera Machine Learning review](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera: Machine Learning Roadmap](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
- [ ] [Machine Learning Distilled](https://code.tutsplus.com/courses/machine-learning-distilled)
- [ ] [BigML training](https://bigml.com/training)
- [ ] [Coursera’s Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
- Taught by Geoffrey Hinton, a pioneer in the field of neural networks
- [ ] [Machine Learning - CS - Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [Creative Applications of Deep Learning with TensorFlow](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info)
- [ ] [Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
- [ ] [Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)
- [ ] [6.S094: Deep Learning for Self-Driving Cars](http://selfdrivingcars.mit.edu/)
- [ ] [6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/index.html)
- [ ] [Coursera’s Deep Learning](https://www.coursera.org/specializations/deep-learning)
## Resources
- [ ] [Absolute Beginning into Machine Learning](https://hackernoon.com/absolute-beginning-into-machine-learning-e90ceda5a4bc)
- [ ] [Learn Machine Learning in a Single Month](https://elitedatascience.com/machine-learning-masterclass)
- [ ] [The Non-Technical Guide to Machine Learning & Artificial Intelligence](https://medium.com/@samdebrule/a-humans-guide-to-machine-learning-e179f43b67a0#.cpzf3a5c0)
- [ ] [Programming Community Curated Resources for learning Machine Learning](https://hackr.io/tutorials/learn-machine-learning-ml)
- [ ] [Best practices rule book for Machine Learning engineering from Google](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [ ] [Machine Learning for Software Engineers on Hacker News](https://news.ycombinator.com/item?id=12898718)
- [ ] [Machine Learning for Developers](https://xyclade.github.io/MachineLearning/)
- [ ] [Machine Learning for Humans🤖👶](https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12)
- [ ] [Machine Learning Advice for Developers](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [Machine Learning For Complete Beginners](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [Getting Started with Machine Learning: For absolute beginners and fifth graders](https://medium.com/@suffiyanz/getting-started-with-machine-learning-f15df1c283ea#.yjtiy7ei9)
- [ ] [How to Learn Machine Learning: The Self-Starter Way](https://elitedatascience.com/learn-machine-learning)
- [ ] [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] [An Honest Guide to Machine Learning](https://medium.com/axiomzenteam/an-honest-guide-to-machine-learning-2f6d7a6df60e#.ib12a1yw5)
- [ ] Enough Machine Learning to Make Hacker News Readable Again
- [Video](https://www.youtube.com/watch?v=O7IezJT9uSI)
- [Slide](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely)
- [ ] [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)
- [ ] [{Machine, Deep} Learning for software engineers](https://speakerdeck.com/pmigdal/machine-deep-learning-for-software-engineers)
- [ ] [Deep Learning For Beginners](https://deeplearning4j.org/deeplearningforbeginners.html)
- [ ] [Foundations for deep learning](https://github.com/pauli-space/foundations_for_deep_learning)
- [ ] [Machine Learning Mindmap / Cheatsheet](https://github.com/dformoso/machine-learning-mindmap)
- Machine Learning courses in Universities
- [ ] [Stanford](http://ai.stanford.edu/courses/)
- [ ] [Machine Learning Summer Schools](http://mlss.cc/)
- [ ] [Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [Cambridge](http://mlg.eng.cam.ac.uk/)
- Flipboard Topics
- [Machine learning](https://flipboard.com/topic/machinelearning)
- [Deep learning](https://flipboard.com/topic/deeplearning)
- [Artificial Intelligence](https://flipboard.com/topic/artificialintelligence)
- Medium Topics
- [Machine learning](https://medium.com/tag/machine-learning/latest)
- [Deep learning](https://medium.com/tag/deep-learning)
- [Artificial Intelligence](https://medium.com/tag/artificial-intelligence)
- Monthly top 10 articles
- [Machine Learning](https://medium.mybridge.co/search?q=%22Machine%20Learning%22)
- [Algorithms](https://medium.mybridge.co/search?q=Algorithms)
- [Comprehensive list of data science resources](http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources)
- [DigitalMind's Artificial Intelligence resources](http://blog.digitalmind.io/post/artificial-intelligence-resources)
- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)
- [Awesome Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification)
- [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection)
- [CreativeAi's Machine Learning](http://www.creativeai.net/?cat%5B0%5D=machine-learning)
- [Roadmap of Machine Learning](https://www.scaler.com/blog/machine-learning-roadmap/)
- [Machine Learning Online Courses](https://classpert.com/machine-learning)
## Games
- [Halite: A.I. Coding Game](https://halite.io/)
- [Vindinium: A.I. Programming Challenge](http://vindinium.org/)
- [General Video Game AI Competition](http://www.gvgai.net/)
- [Angry Birds AI Competition](https://aibirds.org/)
- [The AI Games](http://theaigames.com/)
- [Fighting Game AI Competition](http://www.ice.ci.ritsumei.ac.jp/~ftgaic/)
- [CodeCup](http://www.codecup.nl/intro.php)
- [Student StarCraft AI Tournament](http://sscaitournament.com/)
- [AIIDE StarCraft AI Competition](http://www.cs.mun.ca/~dchurchill/starcraftaicomp/)
- [CIG StarCraft AI Competition](https://sites.google.com/site/starcraftaic/)
- [CodinGame - AI Bot Games](https://www.codingame.com/training/machine-learning)
## Becoming an Open Source Contributor
- [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet)
## Podcasts
- ### Podcasts for Beginners:
- [Talking Machines](http://www.thetalkingmachines.com/)
- [Linear Digressions](http://lineardigressions.com/)
- [Data Skeptic](http://dataskeptic.com/)
- [This Week in Machine Learning & AI](https://twimlai.com/)
- [Machine Learning Guide](http://ocdevel.com/podcasts/machine-learning)
- ### Interviews with ML Practitioners, Researchers and Kagglers about their Joureny
- [Chai Time Data Science](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x), [Audio](http://anchor.fm/chaitimedatascience), [Writeups](https://sanyambhutani.com/tag/chaitimedatascience/)
- [Machine Learning for Beginners - Interviews](https://www.youtube.com/channel/UCdZ0GX-F3ULMKfxtyzSFbaw), [Audio](https://jayshah.buzzsprout.com/)
- ### "More" advanced podcasts
- [Partially Derivative](http://partiallyderivative.com/)
- [O’Reilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast)
- [Not So Standard Deviation](https://soundcloud.com/nssd-podcast)
- ### Podcasts to think outside the box:
- [Data Stories](http://datastori.es/)
## Communities
- Quora
- [Machine Learning](https://www.quora.com/topic/Machine-Learning)
- [Statistics](https://www.quora.com/topic/Statistics-academic-discipline)
- [Data Mining](https://www.quora.com/topic/Data-Mining)
- Reddit
- [Machine Learning](https://www.reddit.com/r/machinelearning)
- [Computer Vision](https://www.reddit.com/r/computervision)
- [Natural Language](https://www.reddit.com/r/languagetechnology)
- [Data Science](https://www.reddit.com/r/datascience)
- [Big Data](https://www.reddit.com/r/bigdata)
- [Statistics](https://www.reddit.com/r/statistics)
- [Data Tau](http://www.datatau.com/)
- [Deep Learning News](http://news.startup.ml/)
- [KDnuggets](http://www.kdnuggets.com/)
## Conferences
- Neural Information Processing Systems ([NIPS](https://nips.cc/))
- International Conference on Learning Representations ([ICLR](http://www.iclr.cc/doku.php?id=ICLR2017:main&redirect=1))
- Association for the Advancement of Artificial Intelligence ([AAAI](http://www.aaai.org/Conferences/AAAI/aaai17.php))
- IEEE Conference on Computational Intelligence and Games ([CIG](http://www.ieee-cig.org/))
- IEEE International Conference on Machine Learning and Applications ([ICMLA](http://www.icmla-conference.org/))
- International Conference on Machine Learning ([ICML](https://2017.icml.cc/))
- International Joint Conferences on Artificial Intelligence ([IJCAI](http://www.ijcai.org/))
- Association for Computational Linguistics ([ACL](http://acl2017.org/))
## Interview Questions
- [ ] [How To Prepare For A Machine Learning Interview](http://blog.udacity.com/2016/05/prepare-machine-learning-interview.html)
- [ ] [40 Interview Questions asked at Startups in Machine Learning / Data Science](https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science)
- [ ] [21 Must-Know Data Science Interview Questions and Answers](http://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html)
- [ ] [Top 50 Machine learning Interview questions & Answers](http://career.guru99.com/top-50-interview-questions-on-machine-learning/)
- [ ] [Machine Learning Engineer interview questions](https://resources.workable.com/machine-learning-engineer-interview-questions)
- [ ] [Popular Machine Learning Interview Questions](http://www.learn4master.com/machine-learning/popular-machine-learning-interview-questions)
- [ ] [What are some common Machine Learning interview questions?](https://www.quora.com/What-are-some-common-Machine-Learning-interview-questions)
- [ ] [What are the best interview questions to evaluate a machine learning researcher?](https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher)
- [ ] [Collection of Machine Learning Interview Questions](http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/)
- [ ] [121 Essential Machine Learning Questions & Answers](https://elitedatascience.com/mlqa-reading-list)
- [ ] [Minimum Viable Study Plan for Machine Learning Interviews](https://github.com/khangich/machine-learning-interview)
## My admired companies
- [ ] [ELSA - Your virtual pronunciation coach](https://www.elsanow.io/home)
gitextract_5t_u0bry/ ├── LICENSE.txt ├── README-fr-FR.md ├── README-pt-BR.md ├── README-zh-CN.md ├── README-zh-TW.md └── README.md
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About this extraction
This page contains the full source code of the ZuzooVn/machine-learning-for-software-engineers GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 6 files (165.6 KB), approximately 50.1k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.