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
32,084 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

DB

May 30, 2024

Great intro to supervised learning (regression & classification). Clear explanation of sigmoid function and decision thresholds. Could benefit from examples & exploring non-linear boundaries.

AA

Apr 29, 2023

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.

Filter by:

4076 - 4100 of 6,053 Reviews for Supervised Machine Learning: Regression and Classification

By Nuno D C

•

Jan 2, 2023

A well and thought out course

By Karthik P

•

Dec 14, 2022

Great for building foundation

By Clément N

•

Nov 9, 2022

This course was just perfect!

By AABID I S

•

Oct 19, 2022

awresome course by andrew ng

By Saurabh T

•

Oct 10, 2022

Very informative and helpful

By Ale S

•

Aug 24, 2022

Andrew Ng is the best teacher

By Mahmoud A

•

Jul 24, 2022

One Of The Best Courses Ever

By Janice C

•

Jul 2, 2022

Prefect course for beginners

By Reza S

•

Aug 27, 2025

You Are the best dear Andrew

By Dinesh B

•

Aug 2, 2025

Best course so far taken!!!!

By Rajarshi C

•

May 30, 2025

Write your review (optional)

By Rhitwik P

•

Apr 3, 2025

Outstanding for fundamentals

By Marwan E D

•

Mar 30, 2025

Very instructive and amusing

By Orapin P

•

Mar 3, 2025

Lecturer is super excellent.

By Zion A

•

Jan 26, 2025

Love it, Andrew is the best!

By 张涵茗

•

Oct 19, 2024

best ML course of the world!

By Babak A

•

Oct 10, 2024

Easy, basic, and fundamental

By Amit A

•

Sep 13, 2024

Taught in best possible way.

By Deleted A

•

Jun 21, 2024

very deep and understandable

By Bison

•

Jun 1, 2024

Just thankful for everything

By Muhammad B A

•

Mar 27, 2024

A great Course for Beginners

By Andy R

•

Mar 26, 2024

Excellent Introduction to ML

By Omkar S

•

Mar 17, 2024

very helpful for learning ML

By shivam k

•

Mar 11, 2024

very well explained concepts

By AjayMalviya

•

Jan 20, 2024

i want to open loked ,my lab