Section outline

• Select section Day 1 – Foundations & Data Processing

  Day 1 – Foundations & Data Processing

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  • Pandas, NumPy for data exploration

  • Data visualization with Matplotlib and Seaborn

  • Introduction to ML with Scikit-learn

  • Lab: Analyze insurance and stock data, prepare datasets
• Select section Day 2 – Regression Techniques

  Day 2 – Regression Techniques

  • Linear, Ridge, Lasso Regression

  • Model evaluation and tuning (R2, RMSE, Cross-validation)

  • Gradient Descent Algorithms

  • Lab: Forecast prices and tune regression models
• Select section Day 3 – Classification & NLP

  Day 3 – Classification & NLP

  • Logistic Regression, Decision Trees, KNN, SVM, Naïve Bayes

  • Model evaluation: AUC, Precision, Recall, Confusion Matrix

  • NLP with SpaCy, TF-IDF, Sentiment Analysis

  • Lab: Classify credit risk, detect fraud, perform text analysis
• Select section Day 4 – Ensemble & Unsupervised Learning

  Day 4 – Ensemble & Unsupervised Learning

  • Ensemble Learning: Bagging, Boosting, XGBoost

  • Dimensionality Reduction: PCA, Feature Selection

  • Clustering: K-means, Hierarchical, HMM

  • Lab: Marketing analysis, PCA on house price data
• Select section Day 5 – Reinforcement Learning

  Day 5 – Reinforcement Learning

  • RL Fundamentals, MDP, Q-learning, Deep RL

  • Tools: OpenAI Gym

  • Lab: Solve Grid World, multi-agent deep learning simulation