Module 5 — Machine learning

• Supervised learning: linear and logistic regression, decision trees, random forest
• Support vector machines, K-nearest neighbours, Naive Bayes
• Unsupervised learning: K-means clustering, hierarchical clustering, PCA
• Feature engineering, encoding, scaling and train-test strategy
• Model evaluation: accuracy, precision, recall, F1, ROC-AUC, cross validation
• Ensemble methods: bagging, boosting, XGBoost

Module 6 — Deep learning and AI

• Neural network fundamentals, activation functions, backpropagation
• TensorFlow and Keras for building and training models
• Convolutional neural networks for image classification
• Recurrent networks and LSTM for sequence data
• Natural language processing: text cleaning, vectorisation, sentiment analysis
• Introduction to large language models and prompt-based applications

Module 7 — Deployment and tools

• Version control with Git and GitHub
• Building a model API with Flask or FastAPI
• Deploying an app with Streamlit
• Working in Jupyter Notebook and Google Colab

Module 8 — Capstone project and placement preparation

• End-to-end project from problem statement to deployed model
• Portfolio and GitHub profile review
• Resume building for analyst and data scientist roles
• Mock interviews covering technical and HR rounds