Machine Learning Tutorial PDF

Machine Learning: Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn patterns from data and improve their performance on tasks without explicit programming. Instead, ML algorithms build models based on training data to make predictions or decisions. Typ...

Machine Learning: Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn patterns from data and improve their performance on tasks without explicit programming. Instead, ML algorithms build models based on training data to make predictions or decisions. Types of Machine Learning: 1. Supervised Learning Definition: Learns from labeled data (input-output pairs). The model maps inputs to known outputs during training. Use Cases: o Classification: Predict discrete labels (e.g., spam detection, image recognition). o Regression: Predict continuous values (e.g., house prices, temperature forecasting). Algorithms: Linear Regression, Decision Trees, Support Vector Machines (SVM), Neural Networks. Example: Predicting student grades based on study hours and past performance. 2. Unsupervised Learning Definition: Finds patterns in unlabeled data without predefined outputs. Use Cases: o Clustering: Group similar data points (e.g., customer segmentation). o Dimensionality Reduction: Simplify data while retaining key features (e.g., PCA for visualization). o Anomaly Detection: Identify outliers (e.g., fraud detection). Algorithms: K-means, Hierarchical Clustering, Autoencoders, t-SNE. Example: Grouping news articles by topics without prior labels. 3. Reinforcement Learning (RL) Definition: An agent learns by interacting with an environment to maximize cumulative rewards (e.g., points, penalties). Use Cases: Game-playing AI (AlphaGo), robotics, autonomous driving. Key Concepts: Exploration vs. exploitation, policy optimization. Algorithms: Q-Learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO). Example: Training a robot to walk by rewarding forward movement. 4. Semi-Supervised Learning Combines small labeled data with large unlabeled data. Useful when labeling is costly. Example: Medical image analysis where only a few scans are labeled. Linear Regression: Linear regression is a supervised learning algorithm used for predicting continuous numerical values based on one or more input variables. It establishes a linear relationship between the dependent variable (target) and one or more independent variables (features). 1. Types of Linear Regression A. Simple Linear Regression It models the relationship between one independent variable (X) and one dependent variable (Y) using the equation: How It Works: 1. Identifying Variables: We have a dependent variable (Y, the thing we want to predict). We have one or more independent variables (X, the things that influence Y). 2. Finding the Best-Fit Line: Overfitting and Underfitting in Machine Learning: Overfitting and underfitting are common problems in machine learning models, including linear regression, that affect how well a model generalizes to unseen data. Definition: Overfitting occurs when a model learns not only the underlying pattern in the training data but also the noise. It performs very well on training data but poorly on test data. Definition: Underfitting occurs when a model is too simple and fails to capture the underlying trend in the data. It performs poorly on both training and test data. Types of Error Functions Used in Linear Regression In linear regression, error functions (also called loss functions) measure how well the predicted values match the actual values. The goal is to minimize the error so the model makes accurate predictions. 1. Mean Squared Error (MSE) 2. Root Mean Squared Error (RMSE) 3. Mean Absolute Error (MAE) 4. Huber Loss 5. Log-Cosh Loss