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
30,553 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

FA

May 24, 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 13, 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!!

筛选依据:

5426 - Supervised Machine Learning: Regression and Classification 的 5450 个评论(共 5,791 个)

创建者 William M

Oct 3, 2023

There should be some way to download formatted notes of key points from the lectures, and to download the submitted quiz content.

创建者 jieyou w

Jun 18, 2023

Very Clear and concise teaching of concepts. Good Effort! Thanks to Andrew and the team behind this course. will sign up for more

创建者 Abhi s

Dec 24, 2022

It's a great course I loved the videos

maths behind the algorithms are explend flowlessly

I wish it focused on practice bit more

创建者 Mohammed T

Jan 11, 2025

it was benefit but i wan't someone explain the code in videos. to make it easy. and the translation not excellent in English.

创建者 Ariel G

Nov 5, 2022

Deberían agregar subtítulos en español para abarcar más personas.

They should add subtitles in Spanish to cover more people.

创建者 Koushik V M

Aug 23, 2022

The lab classes could also be thought as a compulsory part of the course ,otherwise a really good and a recommendable course.

创建者 Bradley R

Jan 6, 2025

really amazing course. It would be helpful to have summaries of course material that are downloadable for future reference.

创建者 Sai G M

Jul 21, 2022

It was fantastic! Andrew is a very good instructor. He made most of the concepts crystal clear while explaining the ideas.

创建者 Sree C

May 18, 2024

make every lab graded instead of optional. so that we can practice every lab seriously and helps for better understanding

创建者 Hasnat A A

Feb 8, 2023

it would be much better if the labs were also explained a bit because there were a lot of things that were quite unseen.

创建者 Elead B

Oct 28, 2024

Great course. I would recommend for someone that has a fair mathematical knowledge. I struggled a bit with the formula

创建者 Niloofar N

May 27, 2025

It would be better if at the end of the course, it gives a more complex project on topics for practice and review.

创建者 KESHAV

Jun 12, 2023

I would like it more if problems like K-means clustering and SVMs were also discussed in the lectures and/or labs.

创建者 Mairi M

May 3, 2024

Excellent teaching and high quality learning materials. The jump between theory and practical was a little steep

创建者 Yousef R

Jul 30, 2022

ts a very helpful course to get into AI, i would sa it could use a bit more coding in the videos to demonistrate

创建者 Moutassi B G

Sep 1, 2024

The basics are perfectly and so simply explained and all is done such you must understand what you are studying

创建者 Shiva T

Feb 20, 2023

Some more practical examples can be included but the course material and topics and ecplaination were great.

创建者 Amit S

Dec 18, 2022

Every concept was explained in a very easy and interesting way. Really liked the course and way of teaching.

创建者 Anukul D

Nov 10, 2022

I actually got the right course at right time and thank you to coursera for providing the course. Hats Off!!

创建者 Raman K P

Dec 18, 2022

Real life dataset use would have been more helpful.

Also, use of scikit-learn could have been explored more.

创建者 Kunal G

Aug 16, 2022

Good One, the course is to the point . Please include linear algebra as it was added in the older version .

创建者 Royston L

Jun 20, 2022

I don't understand why the practice lab code for gradient descent and the lab assignment code is different.

创建者 Fang H

Jan 23, 2024

Explained the complex concepts in very clear and simple way. Labs are very helpful and very well designed.

创建者 Samuel S

Jul 16, 2022

It get's exponetially harder as the weeks go by. This course could really use more programming excercises!

创建者 Kartik T

Jul 25, 2025

It would be better if we could get the downloaded notes of what the Mr. Andrew Ng is showing in the ppt.