ML algorithms include methods like linear regression, decision trees, support vector machines, clustering, and neural networks, each designed to solve different prediction and classification problems.
Explore the core mechanics of decision trees with this beginner-friendly quiz! Test your understanding of how trees split data, the role of features and thresholds, and key metrics like Gini Impurity and Information Gain. Ideal for aspiring data scientists and ML enthusiasts getting started with tree-based models.
Sharpen your grasp of one of the most essential classification algorithms in machine learning! This quiz dives into logistic regression concepts including the sigmoid function, decision boundaries, log loss, one-vs-rest strategy, and its application to both binary and multiclass classification tasks. Perfect for ML interview prep and practical deployment understanding.
Level up your understanding of linear regression beyond the basics! This quiz explores core mathematical intuition and real-world deployment strategies. Topics include multivariate regression, assumptions (linearity, homoscedasticity, multicollinearity), interpretation of coefficients, regularization (Ridge/Lasso), and evaluation metrics like R² and RMSE. Ideal for data science interviews and practical ML pipeline mastery.
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