A comprehensive guide to Machine Learning in Python covering data preprocessing, supervised learning (regression and classification), evaluation and tuning, unsupervised learning, and advanced topics including natural language processing (NLP), deep learning, reinforcement learning, and dimensionality reduction.

Topics Covered

• Data Preprocessing: Importing data, missing values, categorical data, feature scaling, splitting datasets
• Regression: Simple/Multiple/Polynomial Linear Regression, SVR, Decision Tree, Random Forest Regression
• Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree, Random Forest Classification
• Model Selection & Tuning: K-Fold Cross Validation, Grid Search, Boosting (XGBoost)
• Metrics: Confusion Matrix, Accuracy, Precision, Recall
• Clustering: K-Means Clustering, Hierarchical Clustering
• Association Rule Learning: Apriori, Eclat
• Natural Language Processing (NLP): Text processing with Artificial Neural Networks
• Deep Learning: Upper Confidence Bound, Thompson Sampling, Convolutional Neural Networks (CNN)
• Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Dimensionality Reduction: PCA, LDA, Kernel PCA

GitHub Repository

View all machine learning code examples on GitHub