Learning From Data (introductory Machine Learning course)

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Yaser S. Abu-Mostafa 32 Machine Learning Edx

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8.8/10 (Our Score)

Product is rated as #79 in category Machine Learning

This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story–like fashion: What is learning? Can a machine learn? How to do it? How to do it well? Take–home lessons.

Instructor Details

Yaser S. Abu-MostafaProfessor, Electrical Engineering and Computer Science at California Institute of Technology

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Courses : 1

Dr. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology. His main fields of expertise are machine learning and computational finance. He is the co-author of Amazon's machine learning bestseller Learning From Data. Dr. Abu-Mostafa received the Clauser Prize for the most original doctoral thesis at Caltech. He won various Caltech and national teaching awards, including the Feynman Prize in 1996. He is the co-founder of the Neural Information Processing Systems (NIPS) annual conference, the top international conference on machine learning. He chaired a number of conferences on applying machine learning to finance, including Computational Finance (CF-99). In 2005, the Hertz Foundation established a perpetual graduate fellowship named the Abu-Mostafa Fellowship in his honor. Dr. Abu-Mostafa currently serves on a number of scientific advisory boards, and has served as a technical consultant on machine learning for several companies, including Citibank for 9 years. He has numerous technical publications including 3 articles in Scientific American, as well as several keynote lectures at international conferences.

Specification: Learning From Data (introductory Machine Learning course)

Duration	150 hours
Year	2020
Level	Beginner
Certificate	Yes
Quizzes	No

3 reviews for Learning From Data (introductory Machine Learning course)

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★★★★★
Hchan – March 28, 2015
The best online machine learning course I’ve taken (I’ve completed courses by Andrew Ng as well as Hastie and Tibshirani et al), this course covers rigorous theory as well as practical aspects, setting you up for a very solid foundation for future study in machine learning. Assignments are challenging and really require you to understand and engage with the material. Prof Abu Mostafa’s teaching quality is amazing and even highly complex concepts are clearly presented.
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★★★★★
Ronny De Winter – May 5, 2017
Excellent caltech course which runs in parallel with the on site university class.
Good theoretical coverage and applied programming exercises. Highly dedicated teacher and teaching assistants, closely following up the discussion forum. recommended for every serious data scientist. One of the best MOOCs I’ve completed. More elaborated than Andrew Ng’s intro to Machine Learning.
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★★★★★
Kallol Roy – December 5, 2018
This course is like a magic. So good. The concepts are beautifully explained. The instructor of the course is gem. I strongly recommend this course.
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