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Day 1: Foundations and Supervised Learning9Icebreaker and Workshop OverviewIce breaker session & Introduction to the workshop objectives and structureSetting up tools: Python, Jupyter, key libraries (Pandas, Scikit-learn, Matplotlib)Crash Course on Machine Learning ConceptsWhat is Machine Learning?Key Types: Supervised, Unsupervised, ReinforcementApplications across industriesML Pipeline: Data → Model → Validation → DeploymentHands-on:Set up a sample dataset (e.g., Titanic dataset) and explore it using Python (Pandas,Matplotlib).Supervised Learning - RegressionConcepts: Linear Regression, key assumptions, evaluation metrics (R-squared, MSE)Hands-on:Build a Linear Regression model.Preprocess data (handling missing values, scaling).Evaluate and interpret results.Supervised Learning - ClassificationConcepts:Hands-on:Introduction to classification, Logistic Regression.Confusion matrix, Precision, Recall, F1-score.○Implement Logistic Regression.Optimize thresholds and analyze results.Enhancing ModelsRegularization: Ridge, LassoTrain-Test Split and Cross-ValidationHands-on:Implement Ridge and Lasso Regression.Use cross-validation for better model evaluation.Day 2: Advanced Topics and ApplicationsRecap and Q&AReview Day 1 concepts.Clarify doubts or challenges from previous sessions.Tree-based ModelsConcepts:Hands-on:Decision Trees and Random Forests.Avoiding overfitting with hyperparameter tuning.Build a Decision Tree and Random Forest.Cont…of Tree-based ModelsUnsupervised LearningConcepts:K-Means Clustering.Evaluating Clusters (Silhouette Score).Hands-on:Perform clustering on a dataset (e.g., Customer Segmentation).Visualize clusters and evaluate performance.Cont…of Unsupervised LearningReal-world Case StudyWork on an interesting & practical end-to-end project:Data Cleaning and PreprocessingExploratory Data AnalysisModel Building and Evaluation**NOTE: LAPTOP IS MANDATORY, SINCE HANDS-ON SESSIONS NEEDS PROGRAMMING
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Event Venue & Nearby Stays
IIT Madras Research Park, Chennai, India
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