Module 2
Dive deep into machine learning concepts, from data preparation to building and evaluating your first ML models.
Master how computers extract patterns from tabular and structured data. Learn training-testing hygiene, bias-variance trade-offs, and practical Scikit-Learn workflows.
Delve into the core mechanics of machine learning: how statistical algorithms turn raw data into actionable predictive knowledge.
Understand feature engineering, numerical and categorical variables, scaling, normalization, and handling missing data.
Learn why evaluating models on unseen data is essential, and master train-test splits, validation sets, and K-fold cross-validation.
Explore fundamental classification algorithms: Logistic Regression, Support Vector Machines (SVM), and Decision Trees.
Master Simple Linear Regression, Multiple Regression, and Polynomial Regression to forecast continuous outcomes.
Master the confusion matrix, Precision, Recall, F1-Score, ROC curves, and AUC for rigorous model validation.
Diagnose the Bias-Variance tradeoff and tame complex models using L1 (Lasso) and L2 (Ridge) regularization.
Master the unified Scikit-Learn API pattern: Estimator, fit(), predict(), and transform(), along with powerful Pipeline chaining.
Synthesize everything learned in Module 2: ingest raw data, preprocess, tune hyperparameters, evaluate, and export a production model.