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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
27,961 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.

ED

Apr 14, 2025

Loved Andrew Ng's videos and the hands on Jupyter notebook labs! My understanding of ML has significantly improved thanks to this course and going on to the next course to complete ML specialization!!

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5301 - 5325 of 5,347 Reviews for Supervised Machine Learning: Regression and Classification

By Islam I

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

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 17, 2022

Excellent

By vivek N

Aug 31, 2024

ok ok ok

By Krishna S

May 21, 2024

too fast

By Kushagra G

Oct 15, 2024

decent

By Eman E

Feb 9, 2024

good

By Mahesh G

Aug 22, 2023

s

By Richie B

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

Dec 14, 2023

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

By rverker

Jun 23, 2024

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

By Juergen G

Aug 15, 2023

Very nice and helpful for my next career steps.

By Anish I

Jan 30, 2024

too theoretical, not much hands on learning

By Shoaib K

Sep 7, 2024

Fix your week 2 lab exercise the last one

By Houssam T

Sep 21, 2023

i want to see my name on this certeficate

By Natalia S

Feb 25, 2024

no math exercises to practice

By Spikey

Nov 8, 2022

Oversimplified

By Abhinav S

Dec 2, 2022

not good

By Audrey K G

Oct 5, 2024

I encountered persistent errors in Week 2 (2nd course), specifically in the exercise where I was expected to build a neural network using Keras. Despite following the instructions and trying different solutions, the autograder continued to return errors about incorrect activations and model configurations, even when the code appeared to match the expected solution. This issue blocked my progress, and I was unable to complete the assignment successfully.

By Patricia R

Oct 20, 2023

Me paso lo mismo con el curso anterior y es que si no realizas los laboratorios aunque hayas aprobado todo y a pesar de que los test te dan como aprobados dicen que no apruebas y que quedas en 0%, es algo como reiterativo con coursera asi que me rindo, no continuo intentando sacarme certificados por acá