Regression Models: Performance and Evaluation

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Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.