Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
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.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
