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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
30,810 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

FA

May 25, 2023

The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.

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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3976 - 4000 of 5,841 Reviews for Supervised Machine Learning: Regression and Classification

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 22, 2024

very deep and understandable

By Bison

?

Jun 1, 2024

Just thankful for everything

By Muhammad B A

?

Mar 28, 2024

A great Course for Beginners

By Andy R

?

Mar 26, 2024

Excellent Introduction to ML

By Omkar S

?

Mar 18, 2024

very helpful for learning ML

By shivam k

?

Mar 11, 2024

very well explained concepts

By AjayMalviya

?

Jan 21, 2024

i want to open loked ,my lab

By Mr. M S 4 Y B E E

?

Jan 18, 2024

very nice interactive course

By Tharindu L

?

Dec 3, 2023

Best course I have ever done

By Yi-Ting C

?

Aug 21, 2023

I really enjoyed the course.

By Aatka A

?

Jun 5, 2023

Thank Excellent contribution

By Dragos D

?

May 12, 2023

Very clear and to the point!

By Zejd I

?

Apr 7, 2023

Excellent introduction to ML

By Kumar S

?

Mar 30, 2023

Crisp, clear course. Thanks.

By manohar k

?

Mar 6, 2023

The best course in the world

By Andres F R G

?

Dec 30, 2022

Amazing introductory course!

By Alejandro A

?

Nov 9, 2022

Amazing classes and teacher!

By Saravanan P

?

Nov 5, 2022

Very Good!!!! Really Nice...

By Mohammad M

?

Oct 11, 2022

It was perfect

Thanks Andrew

By Sudipta M

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Oct 10, 2022

Simply Amazing, Amazing !!!!

By Naresh A

?

Oct 2, 2022

nice course its very useful

By Diego A S B

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Sep 9, 2022

Me enacnto el curso gracias.