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

4.9
stars
32,127 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.

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4476 - 4500 of 6,058 Reviews for Supervised Machine Learning: Regression and Classification

By Vishnu V

•

Aug 24, 2025

Outstanding course

By Taher M

•

Feb 21, 2025

Best Course for ML

By HARSH K T

•

Feb 21, 2025

Course is amazing.

By Shayan J

•

Feb 13, 2025

i love this course

By Trung P T

•

Jan 28, 2025

perfect, wonderful

By Naveen S

•

Dec 27, 2024

best for begineers

By Vandyck A

•

Nov 9, 2024

Excellent content!

By Alikana A

•

Oct 8, 2024

THANKS TO COURSERA

By Ais K

•

Sep 15, 2024

thanks to coursera

By S G

•

Sep 7, 2024

Excellent teaching

By Talha A

•

Sep 6, 2024

ı love this course

By Hongfei Z

•

Aug 27, 2024

helpful and useful

By Mayank R

•

Jul 30, 2024

very helpul course

By Abhinav P

•

Jul 21, 2024

Simple and perfect

By Alexander S

•

Jun 16, 2024

Greatly explained.

By Mark A

•

Jun 11, 2024

Full of excellence

By Enoch B

•

Apr 11, 2024

very comprehensive

By Bidur W

•

Feb 13, 2024

very nice material

By Valentin B

•

Jan 21, 2024

Pretty cool course

By akash p

•

Jan 11, 2024

exceptional course

By Pratyush S

•

Nov 2, 2023

beautifully taught

By Shawon T J

•

Oct 25, 2023

Extraordinay Class

By Yaw G

•

Oct 4, 2023

Great presentation

By Ritu D

•

Sep 14, 2023

Very useful course

By Kondwani M

•

Aug 23, 2023

Excellent material