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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
25,283 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

JM

Sep 21, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

AD

Nov 23, 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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3201 - 3225 of 4,953 Reviews for Supervised Machine Learning: Regression and Classification

By jaa j

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Jul 31, 2023

So so sooooooo gooooooooooooooddddd

By Umar S

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Jul 28, 2023

it's a amazing content-based course

By Soumik S

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Jul 24, 2023

It was a great learning experience.

By Sufyan A

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Jul 21, 2023

very helpful and full of knowledge.

By Karan M

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Jul 8, 2023

amazing mentorship by respected sir

By 2085_Sreekant

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May 19, 2023

very easy to understand and helpful

By Upendra R

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May 16, 2023

Very good for clearing fundamental.

By pouriya m

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Apr 28, 2023

It was just as perfect as expected.

By Hunain A

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Mar 25, 2023

nice but audio quality was not good

By Hiro

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Mar 23, 2023

Great beginning to Machine Learning

By Alaa A A A

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Feb 4, 2023

Thanks! words can't really describe

By Ivan S

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Jan 12, 2023

Good Material and good expalnations

By ALOK K S

•

Dec 2, 2022

It was phenomenal.

Kudos to the team

By ilma

•

Nov 18, 2022

Concepts are excellently explained.

By Guantai N

•

Nov 6, 2022

Great course and excellent delivery

By Arash M

•

Oct 20, 2022

Great service to mankind for sure!

By Saurabh A

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Sep 7, 2022

Andrew Ng is the best of the best!

By Aryan A P e

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Sep 6, 2022

Easy to follow and quite intuitive

By Amir-Ali M

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Jul 30, 2022

Phenominal course for entering AI.

By Hasibul H

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Jul 18, 2022

One of the best course of Coursera

By Xinyue L

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Jul 14, 2022

A really good high level overview!

By Muhammad I K

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Dec 24, 2024

Amazing explanations and breakdown

By Gajendra K .

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Dec 18, 2024

Teaching method is very excellent.

By Bách C V

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Sep 11, 2024

This course is beginner friendly !

By Harihara s R

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Aug 24, 2024

very helpful thanks for the course