Introduction to Data ScienceIntroduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Ensemble Methods: Random ForestsCovers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Ensemble Methods: Random ForestExplores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
MLPs: Multi-Layer PerceptronsIntroduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.