Chevron Left
返回到 Supervised Machine Learning: Regression and Classification

学生对 DeepLearning.AI 提供的 Supervised Machine Learning: Regression and Classification 的评价和反馈

4.9
32,311 个评分

课程概述

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....

热门审阅

MA

Jan 27, 2025

I've really enjoyed learning about Machine Learning in such a guided way. It will continue to inspire me to learn more about AI. Thank you Andrew Ng, DeepLearning.AI, Standford ONLINE, and Coursera.

AA

Apr 29, 2023

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.

筛选依据:

1701 - Supervised Machine Learning: Regression and Classification 的 1725 个评论(共 6,083 个)

创建者 Md. A S

Dec 13, 2023

Excellent foundation course to understand the core basics and equations behind Supervised machine learning algorithms

创建者 Raghavendra S

Nov 30, 2023

The course is explained very well. The lab assessments are organized in right way to broyp the knowledge of the topic

创建者 Jimmy R

Nov 8, 2023

Super clear and informative! Would absolutely suggest to anyone wanting to a decent understanding of the fundamentals

创建者 Anna M B

Oct 7, 2023

Super! Andrew Ng consistently delivers the best and most inspiring courses on complex subjects like Machine Learning.

创建者 Mohamed E F

Jul 19, 2023

Good course It helped me to deepen my understanding of the implementation of both regression and logistic regression.

创建者 Sambit S

Jul 10, 2023

It's been an incredible learning journey, and I highly recommend this well-crafted course for beginners in the field.

创建者 Abhin B

May 2, 2023

It was a really good course And i feel blessed to have been offered a course like one considering my financial status

创建者 Aly A

Mar 30, 2023

Well organised course and excellent instructor .here i understood alot of concepts was difficult for me to understand

创建者 AhmsOK H

Dec 28, 2022

Andrew Ng is an incredible teacher, making the subject matter and mechanics of this topic, easy to digest and follow.

创建者 J K

Oct 20, 2022

Informative and the interactive labs were great at practicing the material and seeing how it all works for yourself.

创建者 Ali S

Jul 16, 2022

Complete materials/explanations on machine learning fundamentals.

An enjoyable course to participate.

Thanks Dr. Ng

创建者 Dean C

Jun 9, 2025

Very beginner friendly. I breezed through it, without any formal qualifications in mathematics. Very well explained.

创建者 Patrick W O B

May 11, 2025

Very engaging and exemplary that I have learned things that I have not been able to even absorb when I'm in college.

创建者 Karthi M

Feb 16, 2025

Excellent course for beginners, an unique point is duration of videos are cleverly designed to make it more engaging

创建者 Muhaned M

Feb 4, 2025

the cousre is very good but as a coder and ML ENG we needed scikit learn code to be learned and explained , thanks !

创建者 Abdullah N

Sep 20, 2024

well organized material with great quality, loved the step by step approach combined with quizzes and code examples.

创建者 He H

Aug 13, 2024

Good for a starter, I learned the ML concepts before and it also gives me a better understanding of the ML structure

创建者 Adnan R

Jun 27, 2024

Very well structured course. Content was exceptional and the way coursera interface drive the course is exceptional.

创建者 Vansh C

Apr 21, 2024

I have never seen such amount of attention to detail in any course. This course enables me to make quality projects.

创建者 Gregory G

Feb 8, 2024

Excellent explanation and pace. I liked the optional labs and deeper explanations of the math behind the algorithms

创建者 Chirayu R

Jan 22, 2024

The course was good. But the thing that really made me confuse is the unnecessary code in the last weeks assignment.

创建者 Omar E

Aug 30, 2023

Great for entry level with focus on understanding of concepts and getting the big picture behind the science itself.

创建者 Yamoo T

Nov 27, 2022

This course is great and it a definitely suitable course that is prepared for beginners. Everyone should learn it!!!

创建者 Barath U

Sep 25, 2022

It is a wonderful journew with Supervised Machine learning, I am so greatful to take this ML Specialization course.

创建者 Pooria r

Sep 10, 2022

With all my heart and honestly, I can say that it was the best course I saw thanks a lot I appreciate it very much