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

4.9
stars
32,078 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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4726 - 4750 of 6,053 Reviews for Supervised Machine Learning: Regression and Classification

By Ryhan K

•

Feb 3, 2025

Amazing course

By Abdul R

•

Jan 16, 2025

Awesome course

By Daniel V

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Jan 8, 2025

I learned alot

By Mohammad H G

•

Nov 27, 2024

That was great

By Rain L

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Nov 13, 2024

Extremely good

By Biswajit G

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Oct 1, 2024

awesome course

By Bob E

•

Sep 25, 2024

Very thorough.

By i22 0 M A B

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Aug 6, 2024

Learned a lot.

By Cemal G K

•

Jul 29, 2024

it was great!!

By Anumol J

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Jul 29, 2024

Super Course!!

By Muhammad H

•

Jul 7, 2024

love andrew ng

By Renan d B L

•

Jun 28, 2024

Amazing course

By Shahnaz H A

•

Apr 27, 2024

Just Awesome !

By Aman

•

Apr 1, 2024

Well explained

By Yelzhan O

•

Mar 16, 2024

amazing course

By Deleted A

•

Mar 16, 2024

just Awesome!!

By Yassine A

•

Mar 9, 2024

just wonderful

By Abhay P

•

Mar 8, 2024

really helpful

By Arkadiusz J

•

Mar 5, 2024

Thank you ! :)

By Thiago T d S

•

Feb 18, 2024

Great Material

By Bakhtiyor

•

Dec 30, 2023

awesome course

By HIMANSHU B 2

•

Dec 26, 2023

awesome course

By Souvik M

•

Dec 10, 2023

Great Learning

By Md A I

•

Nov 26, 2023

Helpful Course

By Nitin W

•

Nov 21, 2023

Amazing Course