Chapter 1

Introduction to Machine Learning

Definition of ML and comparison with traditional programming

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Types of ML (Supervised, Unsupervised, Reinforcement)

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The Machine Learning pipeline (Data collection to Deployment)

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Overfitting vs Underfitting (Bias-Variance tradeoff)

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Training, validation, and test sets splitting

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Chapter 2

Linear and Logistic Regression

Multiple Linear Regression mechanics and cost function

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Gradient Descent optimization algorithm

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Regularization techniques: Ridge (L2) and Lasso (L1)

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Logistic Regression for binary classification

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Sigmoid function and Maximum Likelihood Estimation

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Chapter 3

Classification Algorithms

K-Nearest Neighbors (KNN) algorithm and distance metrics

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Decision Trees (Entropy, Gini impurity, Information Gain)

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Random Forest and Bagging concepts

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Support Vector Machines (SVM) and Margin maximization

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Naive Bayes classifier and Bayes' theorem application

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Chapter 4

Model Evaluation Metrics

Confusion matrix (TP, TN, FP, FN)

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Accuracy, Precision, Recall, and F1-Score

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Receiver Operating Characteristic (ROC) curve and AUC

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Mean Absolute Error (MAE), Mean Squared Error (MSE), RMSE

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Cross-validation techniques (k-fold CV)

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Chapter 5

Unsupervised Learning

Clustering concepts and business applications

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K-Means clustering and the Elbow method

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Hierarchical clustering and Dendrograms

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Principal Component Analysis (PCA) for dimensionality reduction

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Association Rule Mining (Apriori algorithm, Support, Confidence, Lift)

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