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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
30,707 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

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.

JG

Apr 27, 2024

Es un curso diferente a los de regresión y clasificación donde solo se enfocan en aplicar los algoritmos de Scikit-learn. El profesor Andrew le da un enfoque profundo al detrás que hay en cada modelo.

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5751 - 5775 of 5,812 Reviews for Supervised Machine Learning: Regression and Classification

By Nooreldin N

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Jun 21, 2025

It needs more practical exercises.

By Muhammad U A

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May 22, 2025

kind a slow and not interesting

By Josep B P

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Dec 30, 2023

Step down from the old course.

By NITHISH K

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Jun 30, 2025

the recorded video looks old

By Islam I

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Nov 8, 2024

Concept Only no Beyond that

By Sepehr

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Mar 8, 2024

Easy to follow but useful.

By Fernando B

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Oct 23, 2023

Laboratórios muito básicos

By Aravind N

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Jun 6, 2024

Could cover more concepts

By Kotulski G

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Aug 5, 2024

Not enough practice quiz

By Tanishka G

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Jun 19, 2025

GOOOOOD COURSE

By Harsh S

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Jun 16, 2023

average course

By Biswaraj

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Jul 3, 2024

quite good

By Donia A R A

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Jul 17, 2022

Excellent

By vivek N

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Sep 1, 2024

ok ok ok

By Krishna S

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May 22, 2024

too fast

By Kushagra G

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Oct 15, 2024

decent

By Eman E

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Feb 10, 2024

good

By Mahesh G

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Aug 22, 2023

s

By Richie B

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Nov 30, 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 24, 2024

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

By Rohan S

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Jun 3, 2025

The course should have mentioned that Python knowledge is a prerequisite.

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 30, 2024

too theoretical, not much hands on learning

By Shoaib K

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Sep 7, 2024

Fix your week 2 lab exercise the last one