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Voltar para Machine Learning

Machine Learning, Stanford University

87,847 classificações
22,528 avaliações

Informações sobre o curso

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....

Melhores avaliações

por PT

Sep 01, 2018

Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Thanks!!!!!

por MN

Jun 15, 2016

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

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21,665 avaliações

por partha Pratim Das

Dec 15, 2018


por Krzysztof Pawlaczyk

Dec 15, 2018

Well explained basics of machine learning supported by high quality programming exercises. Good point to start from and get some ML intuition.

por Shahar Katz

Dec 15, 2018

Great course, very informative!

por Anne Heppner

Dec 15, 2018

Thanks a lot for that great course! The content is structured explained very well. It was fun to participate and I would highly recommend it to friends and colleges. The only wish I have is: I would love to see some proof (as an extension-video perhabs) or some more 'transfer'-exercise (I think most questions already directly answered in the videos). Thank you very much again. I really enjoyed the course!

por Piyush bhambhani

Dec 15, 2018

exceptionally worthy, if student does some extra effort

por Peter Neo

Dec 15, 2018

This is definitely a great introductory course to Machine Learning!

por Daniel

Dec 15, 2018

nice course! Teach from basic theory to practical application.

Thank you.

por Sandeep Krishnanunni

Dec 15, 2018

Excellent course .It was really helpful. Thank you for making this.

One small suggestion it would be good if you can add some optional lectures on the math behind some of the algorithm so if people are interested they can view them.

por Farhodjon Abdukodirov

Dec 15, 2018

It seems to me cool but i guess understanding ML is much more difficult than i thought i hope it will be better with real examples.)))))

por Myunggwan Cho

Dec 15, 2018

Great course. It taught me many new concepts about ML. The tips given by the prof on troubleshooting were also extremely helpful.

The difficulty level was spot on, not too hard and not easy.

Some suggestions:

the lecture slides could be given without annotations. One without the writing, one with writing.

There could be more content covering linear algebra.

There could be more coverage for the use of unsupervised learning.

Mini batch / stochastic gradient/ map-reduce: some questions in the exam were confusing and not explained clearly in the lectures.