Machine Learning

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Understanding the fundamental concepts of Machine Learning

Applying the Nearest Neighbours algorithm in Machine Learning problems

Gaining introductory knowledge of conformal prediction and its significance in Machine Learning for reliable predictions

Completing the discussion on full conformal prediction and its applications in Machine Learning

Understanding the risks of overfitting and underfitting in Machine Learning models

Learning about learning curves and their importance in evaluating Machine Learning models

Discussing the method of Least Squares and its advancements, like Ridge Regression and Lasso

Exploring the impact of data preprocessing and parameter selection on the quality of Machine Learning predictions

Studying inductive conformal prediction and its computational efficiency

Applying kernel methods to add flexibility to linear Machine Learning models

Understanding the concepts and applications of neural networks and support vector machines

Learning to use pipelines in Machine Learning workflows with scikit-learn

Studying cross-conformal predictors and their efficiency

Gaining a broad understanding of various prediction algorithms in Machine Learning