├── .gitignore ├── 00. NumPy Basics ├── 1. NumPy Basics.ipynb └── 100_Numpy_exercises_no_solution.ipynb ├── 01. Data Preprocessing ├── 1. Feature Selection.ipynb ├── 2. Scaling, Normalizing.ipynb ├── 3. Feature Extraction.ipynb └── Data.csv ├── 02. Regression ├── 1A. Linear Regression and Gradient Descent(Theory).ipynb ├── 1B. Linear Regression and Gradient Descent .ipynb ├── 1C. Assumptions in Linear Regression and Dummy variables.ipynb ├── 1D. Simple and Multiple Regression using Sci-kit learn.ipynb ├── 2. Backward Elimination.ipynb ├── 3. Polynomial Regression.ipynb ├── 4. Support Vector Regression.ipynb ├── 5. Decision Tree Regression.ipynb ├── 50_Startups.csv ├── 6. Random Forest.ipynb ├── 7. R Squared.ipynb ├── 8. Robust Regression (TheilSen Regressor).ipynb ├── 9. Pipelines in Sklearn.ipynb ├── Position_Salaries.csv ├── Salary_Data.csv ├── Social_Network_Ads.csv └── machine_learning_andrewng │ ├── ex1data1.csv │ └── ex1data2.csv ├── 03. Classification ├── 1A. Logistic Regression and Gradient Descent.ipynb ├── 1B. Deriving Logistic Regression .ipynb ├── 1C. Logistic Regression using Gradient Descent.ipynb ├── 1D. Logistic Regression using sklearn.ipynb ├── 2A. Regularization.ipynb ├── 2B. Regularization on Logistic Regression.ipynb ├── 3. KNN.ipynb ├── 4. SVM.ipynb ├── 5B. Naive Bayes using sklearn.ipynb ├── 5a. Naive Bayes.ipynb ├── 6A. Decision Trees & Information Theory.ipynb ├── 6B. Decision Trees.ipynb ├── 7. Random Forest .ipynb ├── Logistic Regression example 2.ipynb ├── Ridge, and Ridge CV for optimized alpha values.ipynb ├── Social_Network_Ads.csv ├── images │ └── vector_norm.png ├── machine_learning_andrewng │ ├── ex2data1.csv │ └── ex2data2.csv └── machine_learning_lazy │ └── ecommerce_data.csv ├── 04. Clustering ├── 05. Outlier Detection using KMeans.ipynb ├── 1. KMeans.ipynb ├── 2. MiniBatch KMeans.ipynb ├── 3. Hierarchical Clustering.ipynb ├── 4. Image Quantization using Clustering.ipynb ├── Mall_Customers.csv └── naruto.png ├── 05. Model Evaluation ├── 1. Cross Validation and its types.ipynb ├── 3. XGBoost.ipynb ├── Churn_Modelling.csv ├── Confusion Matrix, Precision, Recall.ipynb ├── Grid Search and Randomized Search.ipynb ├── R Squared.ipynb ├── ROC Curve & AUC.ipynb ├── Silhoutte Distance for Clustering.ipynb └── Social_Network_Ads.csv ├── 06. Associate Rule Mining ├── 1. Apriori Algorithm.ipynb ├── 2. Eclat Model.ipynb ├── Market_Basket_Optimisation.csv └── apyori.py ├── 07. Reinforcement Learning ├── 0. Definition and basics.ipynb ├── 1. Upper Confidence Bound.ipynb ├── 2. Thompson Sampling.ipynb ├── 3. Tic-Tac-Toe using Reinforcement Learning.ipynb ├── 4. Markov Decision Processes.ipynb ├── 5. Dynamic Programming- Value Iteration, and Policy Iteration.ipynb ├── 6. Monte Carlo.ipynb ├── 7. Temporal Difference and Q-Learning.ipynb ├── 8. Q-Learning for Ad Optimization - Copy.ipynb ├── 8. Q-Learning for Ad Optimization.ipynb ├── Ads_CTR_Optimisation.csv ├── Thompson_Sampling_Slide.png ├── UCB_Algorithm_Slide.png └── random_selection.py ├── 08. Natural Language Processing ├── 1. Sentiment Analysis.ipynb └── Restaurant_Reviews.tsv ├── 09. Neural Networks ├── 1. Activation Functions.ipynb ├── 2. ANN.ipynb ├── 2A. Backpropagation .ipynb ├── 2B. Neural Networks using Backpropagation.ipynb ├── 3B. Convolutional Neural Networks in TensorFlow.ipynb ├── 3a. Convolutional Neural Networks Theory.ipynb ├── 4. Recurrent Neural Networks and LSTM (Theory).ipynb ├── 5. LSTM .ipynb ├── Churn_Modelling.csv ├── Google_Stock_Price_Test.csv ├── Google_Stock_Price_Train.csv ├── machine_learning_andrewng │ ├── ex3data1.mat │ ├── ex3weights.mat │ └── ex4data1.mat └── regressor.hd5 ├── 10. Dimensionality Reduction ├── 1. Principal Component Analysis.ipynb ├── 2. Linear Discriminant Analysis.ipynb ├── 3. Factor Analysis.ipynb ├── 4. Kernel PCA.ipynb ├── 5. Truncated SVD.ipynb ├── 6. Self Organizing Maps.ipynb ├── 7. Dictionary Learning.ipynb ├── 8. t-SNE.ipynb ├── Credit_Card_Applications.csv ├── Social_Network_Ads.csv ├── Wine.csv └── minisom.py ├── 11. Model Selection and Boosting ├── 1. K-Fold Cross Validation.ipynb ├── 2. Grid Search.ipynb ├── 3. XGBoost.ipynb ├── Churn_Modelling.csv └── Social_Network_Ads.csv ├── 12. Unsupervised Neural Networks ├── 1. Boltzmann Machine.ipynb ├── 2. Autoencoders.ipynb ├── AItRBM-proof.pdf └── rbm.py └── README.md /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maykulkarni/Machine-Learning-Notebooks/HEAD/.gitignore -------------------------------------------------------------------------------- /00. NumPy Basics/1. NumPy Basics.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maykulkarni/Machine-Learning-Notebooks/HEAD/00. NumPy Basics/1. 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