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Вернуться к Машинное обучение

Машинное обучение, Стэнфордский университет

4.9
Оценки: 93,838
Рецензии: 23,753

Об этом курсе

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....

Лучшие рецензии

автор: AD

Apr 22, 2017

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

автор: SB

Sep 27, 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

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Рецензии: 22,907

автор: Rocky Shu

Feb 19, 2019

It is the greatest course in machine learning. Thank Andrew very much for putting all those hard stuff into something understood by me. I appreciate greatly the course and learn many basic concepts and skills in machine learning

автор: Harishankar Behera

Feb 19, 2019

Great course

автор: Rajesh prasannavenkatesan

Feb 19, 2019

top course for beginners

автор: 김진환

Feb 19, 2019

강의내용과 이를 바로 적용해볼수 있는 과제, 질문하기 쉬운 환경과 채점을 쉽게 받을 수 있는 환경이 구축되어있습니다.

처음 과연 내가 이걸 할 수 있을까 했지만 생각보다 수월하게 이수하게 되었습니다.

이 강좌를 이수하기 위해서는 고등학교에서 배우는 행렬과 미분의 정의, 간단한 코딩지식 그리고 약간의 용기가 필요합니다.

автор: Prateek Singh

Feb 19, 2019

theoretically this course is amazing

автор: Mukul Modani

Feb 19, 2019

This course by Prof. Andrew Ng and supported by brilliant mentors, is a fabulous way to learn Machine Learning. It takes you deep into the concepts and algorithms right away, and in a very structured manner. The intensity of classes varies through the weeks, but is mostly manageable even for a full-time working professional with little or no background of ML or AI. The course navigation too is simple, and discussion forums provide a class-like atmosphere. Quizzes and programming assignments are extremely useful. However, I had hoped that there would be an end-to-end exercise towards the end of the course, which would help revise all the course learnings. Highly recommended for those initiating their AI learning journey!

автор:

Feb 19, 2019

Thank you for good lecture. It was very helpful to me. Someday, I want to make these kind of lectures. Thank you Andre

автор: Simon K. Poon

Feb 19, 2019

Excellent coverage of the needed technical information, and the materials are very practical.

автор: Ranim Dewaib

Feb 19, 2019

Excellent instructor! Thank you!

автор: Sunny Sirohi

Feb 19, 2019

Very helpful and enjoyed the session.