Section outline

• Select section Day 1 – ML Foundations & Data Preprocessing

  Day 1 – ML Foundations & Data Preprocessing

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  • Intro to Pandas, Numpy, Scipy, Matplotlib

  • Data cleaning, missing values, time series handling

  • Machine Learning theory and Scikit-learn basics

  • Lab: Stock price and insurance dataset analysis
• Select section Day 2 – Regression Techniques & Model Deployment

  Day 2 – Regression Techniques & Model Deployment

  • Linear, Ridge, Lasso Regression

  • Model evaluation: R2, RMSE, MAE

  • Gradient Descent Variants

  • Model pipelines, cross-validation, hyperparameter tuning

  • Lab: Predict home prices and insurance charges
• Select section Day 3 – Classification & Text Analytics

  Day 3 – Classification & Text Analytics

  • Logistic Regression, Decision Trees, Random Forest, KNN, SVM

  • Evaluation: Accuracy, Precision, Recall, F1, ROC, AUC

  • Text preprocessing: TFIDF, N-grams, LDA, Spacy NLP

  • Lab: Fraud detection, IMDB sentiment analysis
• Select section Day 4 – Ensemble Learning & Clustering

  Day 4 – Ensemble Learning & Clustering

  • Ensemble Methods: Bagging, Boosting, XGBoost, LightGBM

  • Dimensionality Reduction: PCA

  • Clustering: K-means, Hierarchical, Fuzzy C-means, HMM

  • Lab: Marketing analysis, retail sales clustering
• Select section Day 5 – Neural Networks with TensorFlow

  Day 5 – Neural Networks with TensorFlow

  • Neural Networks, Deep Learning Basics

  • TensorFlow, Keras, CNNs

  • Optimization: ADAM, RMSProp, Xavier/He Initialization

  • Lab: MNIST & CIFAR-10 classification, deployment concepts