Hands-on interactive micro-course covering decision trees, model validation, underfitting vs. overfitting, and random forests.
Kaggle's fast-paced interactive course gets you building and validating tabular machine learning models in browser-based coding notebooks. It focuses on decision trees, train/validation splits, Mean Absolute Error, diagnosing overfitting/underfitting, and random forest ensembles.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 2 Hands-On Practice for Modules 2.1 & 2.3
Free tutorials and in-browser Jupyter coding exercises with free completion certificate from Kaggle Learn.
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
Use during Modules 2.1 and 2.3 for rapid hands-on practice with decision trees, random forests, and validation splits.
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.
Practical interactive course covering missing values, categorical variables, pipelines, cross-validation, gradient boosting (XGBoost), and data leakage.
Google's practical, interactive introduction to machine learning fundamentals, linear and logistic regression, gradient descent, generalization, regularization, and neural networks.
Official scikit-learn getting started tutorials and user guide for classical supervised/unsupervised learning, preprocessing, cross-validation, and evaluation metrics.