gitextract_wsbu4mgw/

├── 01. Day 1 - introduction to Python/
│   ├── Dictionaries.ipynb
│   ├── List.ipynb
│   ├── Sets.ipynb
│   └── Touples.ipynb
├── 02. Day 2 - Python Loops and If-else/
│   └── loop-if-else.ipynb
├── 03. Day 3- Python Functions/
│   └── Python Functions .ipynb
├── 04. Day 4 - Python oops/
│   └── Python Oops.ipynb
├── 05. Day 5 - Python Modules/
│   └── Python Modules.ipynb
├── 07. Day 7 - NumPy Random/
│   └── NumPy Random.ipynb
├── 08. Day 8 - Pandas Introduction/
│   └── Pandas Introduction.ipynb
├── 09. Day 9 - Pandas - ipl data analysis/
│   └── Pandas -Analysis of ipl data.ipynb
├── 10. Day 10 - Data Manipulation in Pandas/
│   └── Data Manipulation.ipynb
├── 100. Day 100 - Brain Tumor Classification/
│   └── brain-tumor-classification.ipynb
├── 11. Day 11 - Matplotlib Introduction/
│   └── Matplotlib Introduction.ipynb
├── 12. Day 12 - Matplotlib Line/
│   └── Matplotlib Line.ipynb
├── 13. Day 13 -Matplotlib Bar-pie-Scatter Plot/
│   └── Matplotlib Bar Plot.ipynb
├── 14. Day 14 - Seaborn Introduction/
│   └── Seaborn Introduction.ipynb
├── 15. Day 15 - Seaborn Plots Types/
│   └── Seaborn Plots.ipynb
├── 22. Day 22 - Evaluation Metrics/
│   └── Performance Metrics.ipynb
├── 23. Day 23 - Train-Test-Split/
│   └── Train-Test_Split.ipynb
├── 25. Day 25 - Implementation Cross-Validation Techniques/
│   └── Techniques For cross validation.ipynb
├── 29. Day 29 - Simple Linear Regression/
│   └── simple-linear-regression.ipynb
├── 31. Day 31 - Implementation Of Multiple Linear Regression/
│   └── multiple-linear-regression.ipynb
├── 33. Day 33 - Logistic Regression/
│   └── logistic-regression.ipynb
├── 35. Day 35 - Implementation Of SVM/
│   └── svm-for-classification.ipynb
├── 37. Day 37 - Implementation Of KNN/
│   └── knn-for-classification-mobile-price-prediction.ipynb
├── 39. Day 39 - Implementation Of Decision Tree Algorithm/
│   └── decision-tree-algorithm.ipynb
├── 41. Day 41 - Random Forest Implementation/
│   └── random-forest.ipynb
├── 42. Day 42 - Hyperparameter Tuning Random Forest/
│   └── hyperparameter-tuning.ipynb
├── 43. Day 43 - Ensamble Learning Implementation/
│   └── ensamble-learning.ipynb
├── 45. Day 45 - Implementation of Naive Bayes/
│   └── gaussian-naive-bayes.ipynb
├── 47. Day 47 - Implementation of K Mean Clustering/
│   └── k-mean-clustering.ipynb
├── 49. Day 49 - DBSCAN Clustering Algorithm Implementation/
│   └── dbscan-clustering.ipynb
├── 50. Day 50 - Implementation of Hierarchical Clustering/
│   └── hierarchical-clustering.ipynb
├── 52. Day 52 - implementation of Gaussian Mixture Model/
│   └── gaussian-mixture-model.ipynb
├── 53. Day 53 - Fuzzy C-Mean Clustering implementation/
│   └── fuzzy-c-mean.ipynb
├── 56. Day 56 - Implementation of Apriori Algorithm/
│   └── apriori-algorithm.ipynb
├── 58. Day 58 - PCA Breast Cancer Dataset/
│   └── pca-cancer-dataset.ipynb
├── 60. Day 60 - LDA - WineQuality/
│   └── lda-wine-quality.ipynb
├── 62. Day 62 - Q - Learning BTC Dataset/
│   └── q-learning-bitcoins.ipynb
├── 63. Day 63 - DQN Algorithm Salesforce dataset/
│   └── dqn-algorithm.ipynb
├── 65. Day 65 - Implementation of Genetic Algorithm/
│   └── genetic-algorithm.ipynb
├── 67. Day 67 - Ridge Regression implementation/
│   └── ridge-regression.ipynb
├── 68. Day 68 - Lasso Regression implementation/
│   └── lasso-regression.ipynb
├── 69. Day 69 - ElasticNet Regression/
│   └── elasticnet-regression.ipynb
├── 71. Day 71 - Time Series Analysis implement/
│   └── time-series-analysis.ipynb
├── 72. Day 72 - SimpleTime Series Method/
│   └── simple-time-series-methods.ipynb
├── 73. Day 73 - Exponential smoothing methods/
│   └── exponential-smoothing-methods.ipynb
├── 74. Day 74 - Auto Regressive Methods/
│   └── auto-regressive-methods.ipynb
├── 76. Day 76 - Explainable AI-Shap/
│   └── explainable-ai-shap.ipynb
├── 79. Day 79 - NLP Text Classification/
│   └── nlp-text-classification.ipynb
├── 80. Day 80 - NLP Password Strength/
│   └── nlp-password-strength.ipynb
├── 81. Day 81 - NLP - Spam Detection/
│   └── nlp-spam-detection.ipynb
├── 82. Day 82 - Oversampling - Undersampling/
│   └── oversampling-undersampling.ipynb
├── 83. Day 83 - Twitter Sentiment Analysis/
│   └── twitter-sentiment-analysis.ipynb
├── 84. Day 84 - Emotion Detection - NLP/
│   └── emotion-detection-nlp.ipynb
├── 85. Day 85 - Language Detection - NLP/
│   └── language-detection-nlp.ipynb
├── 95. Day 95 - computer Vision Image processing/
│   └── image-processing.ipynb
├── 96. Day 96 - Image Processing/
│   └── image-processing.ipynb
├── 97. Day 97 - Watermark Using CV/
│   └── watermark-using-cv.ipynb
├── 98. Day 98 - Texture Features Extraction/
│   └── texture-features-extraction.ipynb
└── 99. Day 99 - Lane Detection/
    └── lane-detection-using-cv.ipynb