Linear Regression: BasicsCovers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Model EvaluationExplores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Decision Trees: ClassificationExplores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Machine Learning BiasesCovers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
Machine Learning ReviewCovers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.