Stanford University offers a comprehensive course in Machine Learning that covers the theoretical and practical aspects of this rapidly growing field. Students will learn about various algorithms and models used in ML, including supervised and unsupervised learning, deep learning, and reinforcement learning. The course includes hands-on programming assignments, as well as theoretical concepts such as bias-variance tradeoff, regularization, and optimization. This course is designed for students with a strong background in mathematics and programming who want to develop their expertise in ML.
Machine Learning's Role in Revolutionizing Today's World
CS229: Linear Regression Cost Function
Deciphering W, B, and Cost Function in Linear Regression
Logistic Regression in Data Science: Study Guide
Understanding the Decision Boundary for Logistic Regression
Solving Binary Classification with Logistic Regression
CS229: Regularized Logistic Regression for Data Scientists
Addressing Overfitting in Machine Learning
Overfitting and Underfitting in Machine Learning
Solving Binary Classification with Logistic Regression
Understanding the Decision Boundary for Logistic Regression
Logistic Regression in Data Science: Study Guide
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