Repository for Python Data Science and Machine Learning
Part 1 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Part 2 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 3 - Clustering: K-Means, Hierarchical Clustering
Part 4 - Association Rule Learning: Apriori, Eclat
Part 5 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 6 - Natural Language Processing: Bag-of-words model and algorithms for NLP
Part 7 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 8 - Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 9 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost