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Back to Supervised Machine Learning: Regression and Classification

Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
25,432 ratings

About the Course

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Top reviews

JM

Sep 21, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

AD

Nov 23, 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

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3326 - 3350 of 4,977 Reviews for Supervised Machine Learning: Regression and Classification

By Ankit K

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Dec 15, 2022

I loved the way Andrew ng taught

By Claudius M

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Nov 14, 2022

super intuitive course i love it

By Mohaned M

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Sep 11, 2022

best instructor everrrrrrrrrrrr

By Zaid R

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Jul 23, 2022

AMAZING COURSE. LOVED IT!!!!!!!!

By Diaa E

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Jul 16, 2022

i really have enjoyed the course

By Oloo

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Jan 13, 2025

helpful impressive and exciting

By mahesh b

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Sep 13, 2024

Very good course to start with.

By Nikhil B

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Aug 25, 2024

Best course by the best teacher

By Gurjot S

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Jul 21, 2024

tremendous course to start with

By Debshuvra S

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Jul 9, 2024

The course is literally so good

By Jaider S M Q

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Jun 23, 2024

Es el mejor curso que he tomado

By vincent s

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May 29, 2024

Very clear and useful knowledge

By Rolando R Z C

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Apr 25, 2024

Very carefully designed course.

By Mohamed A

•

Apr 4, 2024

The best course for this field.

By FEDI L

•

Mar 22, 2024

Extremely inspiring and helpful

By Peter V E

•

Mar 19, 2024

Great course! Very well taught.

By Deleted A

•

Feb 16, 2024

this course is very interesting

By Joseph D

•

Dec 6, 2023

Very well organized and taught!

By Jimi W

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Sep 4, 2023

Everything is explained clearly

By Liaqat N

•

Sep 2, 2023

It was fun learning this course

By Reza N

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Aug 22, 2023

The best course for learning ML

By 陈爽羽

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Aug 21, 2023

非常棒的一门课，让我初步了解了机器学习，十分感谢各位老师的帮助

By Dilyana D

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Aug 1, 2023

Very well paced and structured!

By Nirjara B

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Jun 22, 2023

Very good course for beginners.

By Maulaya R

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May 20, 2023

Very good mathematical approach