Hands on Machine Learning Using Python

An attempt to explore the different models in Scikit-Learn and cover the fundamentals of Machine Learning. These are the basic models and techniques used in Machine Learning. Will soon update the readme with usecase and details of each model

The code is structured in the following way:

Part 1 - Data Preprocessing

Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 - Clustering: K-Means, Hierarchical Clustering

Part 5 - Association Rule Learning: Apriori

Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost