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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
27,808 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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5151 - 5175 of 5,329 Reviews for Supervised Machine Learning: Regression and Classification

By Bhanu P G S

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Dec 30, 2023

Codes re directly coded and not explained

By Balasubbaiah L

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Nov 9, 2024

required more realtime usecase scenarios

By Sabtain K

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Mar 25, 2024

Wish there were a bit more project work.

By Sandeep T

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Jun 25, 2023

Gradient descent concepts were not clear

By Gavin H

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Apr 25, 2023

Slow, but in depth. Well done in total.

By Harinda F

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Sep 29, 2022

programming assignments are very basic

By AMMARI h

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Aug 12, 2023

Great course , explanations were clear

By Pawan B

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Jun 28, 2023

the coding lab test were the best part

By Shaikh I

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May 16, 2023

Good content and excellent explanation

By Surendra A

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Nov 29, 2022

Great course, for basic understanding.

By Zlatko J

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Sep 21, 2022

more coding exercises would be great!

By Pulimi Y

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Jul 28, 2022

great course to start Machine Learning

By Kavya M

•

Dec 24, 2024

course was good but very complicated

By Ajay C

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Sep 21, 2024

Very useful to my career development

By geet c

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Mar 31, 2023

Really Good Course to start of with

By Kunal E 2 P U

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Aug 21, 2022

The labwork can be much much better

By Kritagyay U

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Aug 28, 2023

The course structure is awesome .

By Abhishek K

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Aug 11, 2023

Optional part should be explained

By Amirhossein N

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May 26, 2023

thanks to Andrew NG. It was well.

By 20ECE053 M A I

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Sep 24, 2022

Really interesting, Good teacher

By Ishit A

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Aug 21, 2023

great explanation for beginners.

By MD A

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Nov 23, 2024

Great Course, enjoyed to learn!

By Mohit Y

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Jun 9, 2023

I expected more rigrous course.

By WONG, L H K

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May 23, 2023

No enough mathematical concepts