Introduction to Machine Learning

Related lectures (32)

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

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

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

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

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers linear regression training to find the best line for given data points, essential for predicting house prices.

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.