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

4.9
stars
27,938 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

ED

Apr 14, 2025

Loved Andrew Ng's videos and the hands on Jupyter notebook labs! My understanding of ML has significantly improved thanks to this course and going on to the next course to complete ML specialization!!

AD

Nov 24, 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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3551 - 3575 of 5,346 Reviews for Supervised Machine Learning: Regression and Classification

By Idan D

•

May 7, 2024

Great course, efficient lectures

By JITENDRA K (

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Mar 20, 2024

good knowladge about this course

By Umer K

•

Dec 25, 2023

Best course to satrt learning ML

By Sumaya A

•

Dec 15, 2023

Excellent course for ML Beginner

By CHENGHONG L

•

Dec 14, 2023

The best machine learning course

By Kishan T

•

Nov 25, 2023

It is the best course! the best!

By Alexander L M

•

Oct 27, 2023

Fantastic course and instructor.

By Sara N

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Sep 1, 2023

You are a good teacher Andrew Ng

By MAI A

•

Aug 11, 2023

Wonderful explaining and content

By Prerana b

•

Jun 12, 2023

very insightful and interesting.

By Ritik R

•

Jun 8, 2023

must do course if beginner in ml

By LALIT C

•

Apr 3, 2023

Intuive and Wonderful Experience

By Youcef M

•

Mar 11, 2023

It is a very instructive course.

By MADDILA S A 2

•

Feb 27, 2023

GOOD COURSE FOR MACHINE LEARNING

By MINHAZ C

•

Feb 1, 2023

I have learned a lot! Thank you!

By Ayaz A

•

Dec 29, 2022

The best ever course on internet

By Hansen C

•

Dec 28, 2022

Great lecturer with great course

By Ankit K

•

Dec 15, 2022

I loved the way Andrew ng taught

By Claudius M

•

Nov 14, 2022

super intuitive course i love it

By Mohaned M

•

Sep 11, 2022

best instructor everrrrrrrrrrrr

By Zaid R

•

Jul 23, 2022

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

By Diaa E

•

Jul 16, 2022

i really have enjoyed the course

By Staffan L

•

Mar 22, 2025

Well presented and interesting.

By Oloo

•

Jan 14, 2025

helpful impressive and exciting

By mahesh b

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Sep 13, 2024

Very good course to start with.