Machine Learning Guide

Welcome to the Machine Learning (ML) Guides! Explore in-depth resources on a range of machine learning models, each paired with interactive demos and practical exercises. Starting with a general introduction, each subsequent chapter delves into a specific model, offering insights into its underlying theory, step-by-step implementation, and real-world applications.

Supervised Learning (SL)

• General Introduction - An overview of machine learning, its types, and basic concepts. Available
• Linear Regression - Learn how linear regression predicts continuous values based on input features. Available
• Logistic Regression - Understand logistic regression for binary and multiclass classification tasks. Partially Available
• Decision Trees - Explore how decision trees can solve both classification and regression problems. Partially Available
• Random Forests - Discover the power of ensemble learning with random forests for robust predictions. Partially Available
• Support Vector Machines (SVM) - Understand SVMs for high-dimensional classification and regression. Partially Available
• K-Nearest Neighbors (KNN) - Learn about KNN, a simple yet powerful classification algorithm based on proximity. Partially Available
• Naive Bayes - A fast, probabilistic classifier ideal for text-based and categorical data. Coming Soon
• Gradient Boosting - Learn how boosting techniques enhance model accuracy in complex datasets. Coming Soon
• Extreme Gradient Boosting (XGBoost) - Dive into XGBoost, a highly efficient and scalable gradient boosting algorithm. Coming Soon
• Light Gradient Boosting (LightGBM) - Learn about LightGBM, an optimized version of gradient boosting for large datasets. Coming Soon
• Neural Networks - Explore neural networks, the foundation of deep learning for complex pattern recognition tasks. Coming Soon

Unsupervised Learning (USL)

• Principal Component Analysis (PCA) - A technique to reduce dimensionality while preserving important data features. Coming Soon
• K-Means Clustering - A method to group similar data points into clusters without labeled data. Coming Soon
• Hierarchical Clustering - A clustering technique for organizing data points in a tree-like structure. Coming Soon
• Association Rule Learning - Techniques like Apriori for finding relationships in data, often used in market basket analysis. Coming Soon
• Anomaly Detection - Techniques to identify unusual data points in a dataset, useful for fraud detection. Coming Soon

Reinforcement Learning (RL)

• Deep Q-Learning - A neural network-based approach to learn optimal actions in complex environments. Coming Soon
• Policy Gradient Methods - Algorithms that optimize the agent’s behavior by directly adjusting its policy. Coming Soon
• Monte Carlo Methods - Techniques for making decisions based on random sampling and long-term rewards. Coming Soon
• SARSA - A reinforcement learning algorithm that updates the agent’s behavior based on state-action-reward-state-action. Coming Soon