Module 1: Introduction to Machine Learning

• Overview of ML concepts and types (supervised, unsupervised, reinforcement learning)

• Key terminology: features, labels, training/testing data, overfitting, bias-variance trade-off

• Real-world applications of ML

Module 2: Setting Up Python for ML

• Installing Anaconda and Jupyter Notebook

• Introduction to Pandas, NumPy, Matplotlib, and Seaborn

• Basic data import and exploratory data analysis (EDA)

Module 3: Data Preprocessing and Feature Engineering

• Handling missing data and categorical features

• Data normalization and scaling techniques

• Feature selection and extraction

• Train-test split and cross-validation