What is Machine Learning?

• Definition and real-world applications.
• Types of ML: Supervised, Unsupervised, and Reinforcement Learning.

Machine Learning Workflow

• Data collection and preprocessing.
• Model training, evaluation, and deployment.

Tools and Libraries

• Python for Machine Learning.
• Essential libraries: NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.

Linear Regression

• Concept of line fitting and equation (y = mx + c).
• Implementation using scikit-learn.
• Metrics: Mean Squared Error (MSE), R² Score.

Logistic Regression

• Classification concepts and sigmoid function.
• Binary and multiclass classification.
• Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC.

Decision Trees

• Tree structure, nodes, and splitting criteria.
• Implementation and visualization.

Naive Bayes

• Bayes’ Theorem and feature independence.
• Applications in text classification (e.g., spam detection).

k-Nearest Neighbors (k-NN)

• Distance metrics (Euclidean, Manhattan).
• Implementation and performance evaluation.

Support Vector Machines (SVM)

• Decision boundary and kernel trick.
• Hyperparameter tuning (C, Gamma).

Clustering

• k-Means, Hierarchical, and DBSCAN algorithms.

Dimensionality Reduction

• Principal Component Analysis (PCA).
• t-SNE for visualization.

Association Rule Learning

• Apriori algorithm for market basket analysis

Basics of Reinforcement Learning

• Agent, environment, actions, and rewards.

Markov Decision Process (MDP)

• States, actions, transition probabilities, and rewards.

Algorithms

• Q-Learning and Deep Q-Learning.