Chevron Left
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.

Filter by:

3451 - 3475 of 4,976 Reviews for Supervised Machine Learning: Regression and Classification

By Gerd D B

•

Sep 4, 2022

Awesome guys! Thanks a lot!

By Yousef E

•

Aug 26, 2022

Best.

Thank you Dr.Andrew NG.

By 200_AKASH S

•

Oct 1, 2024

very helpful and conceptual

By charith_neeli

•

Sep 3, 2024

Excellent Work By Andrew Ng

By Đinh N L T

•

Aug 20, 2024

very good, very informative

By Mohamed H S J

•

Jul 30, 2024

i love this content so much

By Feroz K

•

Jul 17, 2024

fantastic, very informative

By Ankit

•

Jul 11, 2024

Very useful and interesting

By Akash S

•

Jun 16, 2024

Very good and informational

By Nishanth R

•

Mar 24, 2024

very useful and interactive

By Koyyada D

•

Mar 12, 2024

Andrew sir you are awesome.

By Chikkala L N

•

Feb 3, 2024

excellent math involve ment

By Jimmy H

•

Feb 1, 2024

great course and instructor

By Idir b

•

Jan 24, 2024

Really high quality course.

By MD F K

•

Dec 30, 2023

Andrew Ng is the GOAT of ml

By Maaz A

•

Dec 15, 2023

superb course for beginners

By Gopal G

•

Dec 8, 2023

Great, Interactive course..

By Harshit K

•

Nov 25, 2023

best course available in ML

By Omer N

•

Sep 10, 2023

Exceptionally great teacher

By marwa o

•

Jul 22, 2023

very simple and easy course

By Nandan K

•

Jul 21, 2023

please include some project

By Muhammad H M

•

Jul 10, 2023

Great concepts and learning

By Aditya G

•

Jul 7, 2023

really good for beginners!!

By Derek 典

•

May 20, 2023

easy to understand! love it

By Abiton P

•

May 18, 2023

best start up for beginners