1️⃣ Types of Machine Learning Supervised Learning – Uses labeled data to train models. Examples: Linear Regression, Decision Trees, Random Forest, SVM Unsupervised Learning – Identifies patterns in unlabeled data. Examples: Clustering (K-Means, DBSCAN), PCA Reinforcement Learning – Models learn through rewards and penalties. Examples: Q-Learning, Deep Q Networks 2️⃣ Key Algorithms Regression – Predicts continuous values (Linear Regression, Ridge, Lasso). Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes). Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN). Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA). 3️⃣ Model Training & Evaluation Train-Test Split – Dividing data into training and testing sets. Cross-Validation – Splitting data multiple times for better accuracy. Metrics – Evaluating models with RMSE, Accuracy, Precisin, Recall, F1-Score, ROC-AUC. 4️⃣ Feature En...
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