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機械学習, スタンフォード大学(Stanford University)

4.9
102,610件の評価
25,486件のレビュー

このコースについて

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. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

人気のレビュー

by SS

May 17, 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

by RD

Mar 31, 2018

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days

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24,631件のレビュー

by Ming

May 25, 2019

Very clear show some basic concept in this region and give me a good start , thanks

by nikhil bharadwaj

May 25, 2019

Beautiful explanations with appropriate examples. Now i know why this is the most recommended course by my friends.

by Vikrant Tyagi

May 25, 2019

Great introductory course on machine learning. Professor Andrew Ng is really great and articulate.

by

May 25, 2019

Well defined teaching process I have ever seen. But don't go with octave. Use python , c or java. So that it will be much easier in implementation for projects.

by Sanket Verma

May 25, 2019

This course builds the right and strong foundation for machine learning. Highly recommended. Just as CS50 is for computer science CS229 is for machine learning. Wholeheartedly thanks to Andrew NG for this one.

by Mihael Marović

May 25, 2019

Very nice. Maybe there should be some references to cover some material in depth.

by Vladimir Zakharov

May 25, 2019

Many thanks to prof. Andrew Ng for his efforts.

by Sam Bhatta

May 25, 2019

Dr. Ng's explanations were greatly engaging and lucid, making even the complex concepts easier to comprehend! Thank you Dr Ng, and thanks to your TAs, for making the quizzes and programming exercises interesting and grounding the concepts.

by Yash Baheti

May 25, 2019

This course was very well taught. There was a impressive focus on the basics and fundamentals of each topic. The lecture slides encapsulates the topics well and thus there was no such need of making my own notes which speeded up the learning process ;).

by Adam Turowicz

May 25, 2019

Great course! I was already introduced to machine learning concepts before taking it, but it helped me to understand how to choose proper algorithms and also how to evaluate and debug them. Also, it helped me to understand how neural networks (especially backpropagation) work, which area has been fuzzy for me before.