Andrew Ng - Machine Learning - Stanford University

Andrew Ng - Machine Learning - Stanford University
1.48 GB
English | Genre: eLearning
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
In this course, you'll learn about some of the most widely used and successful machine learning techniques. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well. This is an "applied" machine learning class, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations.

Familiarity with programming, basic linear algebra (matrices, vectors, matrix-vector multiplication), and basic probability (random variables, basic properties of probability) is assumed. Basic calculus (derivatives and partial derivatives) would be helpful and would give you additional intuitions about the algorithms, but isn't required to fully complete this course.
Topics include:

1. Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
2. Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
3. Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

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