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Related lectures (31)

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Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

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

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Explores binary classification by regression, decision functions, and various cost functions.