Practical interactive course covering missing values, categorical variables, pipelines, cross-validation, gradient boosting (XGBoost), and data leakage.
Building directly on Intro to Machine Learning, this course teaches production-grade tabular ML habits: imputing missing values without leaking validation statistics, encoding categorical features, constructing clean scikit-learn pipelines, k-fold cross-validation, gradient boosted trees, and spotting subtle target/train-test leakage.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 2 Practical Workflow Course (Modules 2.3 & 2.4)
100% free interactive lessons and coding notebooks on Kaggle.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 2: Machine Learning From Scratch
Complete alongside Modules 2.3 and 2.4 to master cross-validation, gradient boosting, and preventing data leakage.
Learning paradigms, train/validation/test splits, parameters vs. hyperparameters, overfitting, the bias-variance tradeoff, regularization, and data leakage prevention.
K-Nearest Neighbors, Decision Trees, Random Forests, Naive Bayes, SVMs, K-Means Clustering, PCA, and Gradient Boosting.
Classification and regression metrics, confusion matrices, precision-recall tradeoffs, ROC-AUC, baseline comparisons, and leakage detection.
Hands-on interactive micro-course covering decision trees, model validation, underfitting vs. overfitting, and random forests.
Official scikit-learn getting started tutorials and user guide for classical supervised/unsupervised learning, preprocessing, cross-validation, and evaluation metrics.
Google's practical, interactive introduction to machine learning fundamentals, linear and logistic regression, gradient descent, generalization, regularization, and neural networks.