1. Introduction to AI, ML & Data Science ● What is Artificial Intelligence ● What is Machine Learning and how it works ● Differences between AI, ML, DL & Data Science ● Real-world applications of AI & ML ● Career opportunities in AI/ML 2. Python Programming for AI/ML ● Python basics: variables, loops, conditions, functions ● Lists, dictionaries, tuples, and sets ● Numpy for numerical computing ● Pandas for data manipulation ● Matplotlib and Seaborn for data visualization 3. Data Handling & Preprocessing ● Reading datasets (CSV, Excel, JSON) ● Handling missing values ● Encoding categorical data ● Feature scaling: normalization & standardization ● Train-test split

1. Core Machine Learning Algorithms ● Supervised learning: Linear & Logistic Regression ● Unsupervised learning: K-Means, Hierarchical Clustering ● Decision Trees and Random Forest ● Support Vector Machines (SVM) ● Model evaluation: Accuracy, Precision, Recall, F1-Score 2. Model Training & Evaluation ● Splitting datasets: train, test, validation ● Cross-validation techniques ● Confusion matrix and ROC-AUC ● Overfitting and underfitting concepts ● Saving and loading ML models using pickle 3. Projects & Hands-on Implementation ● House Price Prediction using Linear Regression ● Titanic Survival Prediction using Logistic Regression ● Customer Segmentation using K-Means ● Student Mini Projects & Assignments ● Capstone Mini Project at end of course