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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
27,885 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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4826 - 4850 of 5,339 Reviews for Supervised Machine Learning: Regression and Classification

By vinod t

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Aug 12, 2022

good

By Sayem B

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Jul 26, 2022

good

By Амирбек Н Ғ

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Apr 21, 2025

фчы

By Carloka B A S M

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Oct 28, 2024

...

By Onalenna E M

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Jun 8, 2024

10.

By Daniel D

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Apr 16, 2024

amz

By Márcio A C B Z

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Mar 19, 2024

Top

By Markelov G M

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Nov 23, 2023

Top

By Ahmed A

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Jul 19, 2023

wow

By do t v

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Mar 1, 2025

ok

By Vikas

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Oct 14, 2024

no

By Edeki M E

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Sep 29, 2024

ok

By 石天辰

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Apr 18, 2024

很好

By Putu G P A

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Apr 3, 2023

:)

By 王家乐

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Dec 31, 2022

很好

By atiye g

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Nov 12, 2022

ok

By Jaber

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Aug 10, 2022

<3

By Shashi S

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Jan 9, 2025

5

By bowen y

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May 25, 2024

好

By Fatema K A

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Apr 23, 2024

-

By Mohamed A A

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Apr 16, 2024

♥

By Ujjawal J

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Aug 11, 2023

m

By SAI R

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Jun 17, 2023

.

By Luiz A

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Sep 13, 2022

v

By William W

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Sep 13, 2023

It's fine if you have a relatively strong background in implementing "multi-step" mathematics in Python. I would not say this is for an actual beginner. Maybe not even someone who is concurrently learning Python AND this course at the same time. I'm rating the course highly for the subject matter that it presents, but I struggled IMMENSELY during the practicals (you actually code key portions of the definitions--but not the entire Jupyter notebook).

The video portion is awesome. Andrew ("Dr. Ng"?) provides an excellent "plain-english" down-to-earth explanation of the math behind the algorithms. The code, however... Well, let's just say it FEELS like one of those art-instruction jokes: "Drawing an Owl: First you start with two circles. Second, you draw the rest of the (explicative) owl."

I don't really think I have much of a furture in anything remotely involving math and programming. After this course, I honestly feel more inclined to stick to dumping data with SQL and letting the grownups slice and dice it.

Bottom line: I've been in IT for over two decades and have alot of (outdated) skills in my toolbag, but this course brought me to tears of feeling like my brain is finally starting to slip away. I just can't learn stuff the way I used to. I don't know. Maybe this will all make more sense after I've slept on it.