Description

Machine Learning Algorithms – Concepts, Types & Examples --- 1. Introduction Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed. Goal: Build models that generalize well on unseen data. Key Steps in ML Process: 1. Data Collection 2. Data Preprocessing (cleaning, normalization, encoding) 3. Feature Selection/Engineering 4. Model Selection 5. Training & Testing 6. Evaluation (Accuracy, Precision, Recall, F1-score) --2. Types of Machine Learning TypeDescription Example Algorithms Supervised Learning Learn from labeled data Regression, Decision Trees, SVM Linear Unsupervised Learning Discover hidden patterns in unlabeled data K-Means, PCA Reinforcement Learning Learn by interacting with an environment through rewards Q-Learning, Deep QNetwork 3. Supervised Learning Supervised ML uses input-output pairs to train the model. A. Regression (Continuous Output) Used when output is a continuous value. Linear Regression: Fits a straight line y = mx + c Polynomial Regression: Fits nonlinear relationships Ridge/Lasso Regression: Add regularization to avoid overfitting Example: Predicting house prices, temperature, sales. B. Classification (Categorical Output) Used when output is a class label. Logistic Regression: For binary classification Decision Tree: Splits data using decision rules Random Forest: Ensemble of trees Support Vector Purchase document to see full attachment