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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
25,432 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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4926 - 4950 of 4,976 Reviews for Supervised Machine Learning: Regression and Classification

By Ganesh K

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Jul 26, 2023

Course is good but lab assignments and exercises are less.

By shivam s

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Feb 26, 2023

Should also some small projects for better understanding!

By Faezeh T

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Sep 25, 2024

it would be better if there were more coding experiment.

By Mohamed a a

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Jul 23, 2023

good course but i wish it was more project oriented

By kunal s

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Jun 12, 2023

please include projects in this course.

By Boris A

•

May 26, 2023

A lot of theory and a bit of practice

By Josep B P

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Dec 30, 2023

Step down from the old course.

By Islam I

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Nov 8, 2024

Concept Only no Beyond that

By Sepehr

•

Mar 7, 2024

Easy to follow but useful.

By Fernando B

•

Oct 23, 2023

Laboratórios muito básicos

By Aravind N

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Jun 6, 2024

Could cover more concepts

By Kotulski G

•

Aug 5, 2024

Not enough practice quiz

By Harsh S

•

Jun 16, 2023

average course

By Biswaraj

•

Jul 3, 2024

quite good

By Donia A R A

•

Jul 16, 2022

Excellent

By vivek N

•

Aug 31, 2024

ok ok ok

By Krishna S

•

May 21, 2024

too fast

By Kushagra G

•

Oct 14, 2024

decent

By Eman E

•

Feb 9, 2024

good

By Mahesh G

•

Aug 22, 2023

s

By Richie B

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Nov 29, 2022

Great course if you know how to program, but you really need a python background to appreciate it.

By Daniele d b

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Dec 14, 2023

quite too basics...too few practical exercise, just scratching the surface of ML

By rverker

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Jun 23, 2024

Impossible to do the exercises if you don't pay. The course is interesting.

By Juergen G

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Aug 15, 2023

Very nice and helpful for my next career steps.

By Anish I

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Jan 29, 2024

too theoretical, not much hands on learning