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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
27,164 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.

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.

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3526 - 3550 of 5,245 Reviews for Supervised Machine Learning: Regression and Classification

By 陈爽羽

•

Aug 21, 2023

非常棒的一门课，让我初步了解了机器学习，十分感谢各位老师的帮助

By Dilyana D

•

Aug 1, 2023

Very well paced and structured!

By Nirjara B

•

Jun 22, 2023

Very good course for beginners.

By Maulaya R

•

May 20, 2023

Very good mathematical approach

By Ragul A

•

Apr 9, 2023

Very useful, Easy to understand

By Jihan K

•

Mar 22, 2023

Complex but interesting course!

By Luc B

•

Jan 22, 2023

Super formation ! Je recommande

By Vadim P

•

Jan 7, 2023

Great course, highly recommend.

By Abhay K

•

Dec 27, 2022

I love Andrew NG teaching style

By Ella N

•

Nov 18, 2022

simple and fun, smart and clear

By Shuo L

•

Nov 17, 2022

The explanation is super clear.

By Milos I

•

Nov 16, 2022

Best specialization on coursera

By Vincent C

•

Nov 5, 2022

Tremendous course thanks a lot.

By Hamidreza M

•

Oct 17, 2022

It was a really useful course.

By Sayak H

•

Oct 6, 2022

excellent and easy explanation

By gautam g

•

Jul 23, 2022

Excellent course for beginner.

By PhucVTHE161744

•

Jul 12, 2022

Super useful for beginner in AI

By Haouchine Y

•

Jan 4, 2025

Excellent course for beginners

By Syed W B U

•

Dec 15, 2024

Nice course start is difficult

By Amir K

•

Sep 11, 2024

Excellent course for beginners

By Nima R

•

Aug 12, 2024

It's good course for beginners

By Roaa M

•

Aug 4, 2024

Very good, fun and very useful

By Chakradhar S

•

Jul 26, 2024

excellent profound and amazing

By HASANCAN A

•

Jul 14, 2024

Excellent and perfect course .

By Arshdeep K

•

Jun 25, 2024

Everything was well explained.