返回到 Supervised Machine Learning: Regression

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38 条评论

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
By the end of this course you should be able to:
Differentiate uses and applications of classification and regression in the context of supervised machine learning
Describe and use linear regression models
Use a variety of error metrics to compare and select a linear regression model that best suits your data
Articulate why regularization may help prevent overfitting
Use regularization regressions: Ridge, LASSO, and Elastic net
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics....

NV

Nov 15, 2020

Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey.

AF

Nov 6, 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

筛选依据：

创建者 Christopher W

•Jan 25, 2021

Really good course but it is whistle-stop through the methods. I strongly recommend getting a book to accompany the course if you are relatively new just so you can cross reference some of the methods and functions.

I found some of the examples a little more difficult to apply to the course work because of how they were demonstrated in the lab. This is NOT a bad thing, all good learning, but when you're trying to unpack things it's good to have another reference source handy.

创建者 Nick V

•Nov 16, 2020

Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey.

创建者 Abdillah F

•Nov 7, 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

创建者 Minh L

•Sep 30, 2021

very detailed. However, it is better if the gradient decent has its lesson.

创建者 Nir C

•Oct 8, 2021

Great course! Covered everything I wished to learn!

创建者 Nancy C (

•Apr 24, 2021

Before taking this course, I tested similar courses offered by other institutes or universities. I am glad that I chose IBM because it has a good balance of concepts and applications. I learned a lot from this course. and will be using what I learned in analyzing experimental and survey data.

I gave this course a 4 instead of 5 because there was insufficient explanation on the different evaluation metrics.

创建者 michiel b

•Feb 15, 2021

Good overview of the different regression models and the theory behind them. Could be a bit more attention to common pittfalls and type and size of problems which are usually addressed by these methods.

创建者 Kalliope S

•Jun 24, 2021

The balance between theory and application is such that both are left quite poorly covered. One does not get an understanding of how algorithms work, explanations focus on 'intuititve' understanding. At the same time, the coding part is not particularly detailed, either. Moreover, there are several mistakes in videos, quizzes and jupyter lab books. I would not recommend this course.

创建者 Minhaj A A

•Sep 22, 2021

The course covered various aspects of regression modelling in good detail and the practice notebooks were also very helpful in implementing and reinforcing the learnings of course. Though the subject matter is quite wide, efforts were made by the instructor to cover most of them.

创建者 serkan m

•May 3, 2021

Thanks very much for this great course. It is comprehensive and intuitive in terms of Regression analysis. It covers all the necessary tools for an essential and sufficient application of Regression analysis.

创建者 MAURICIO C

•Mar 25, 2021

It was an exceedingly difficult for me, sometimes JSON files under Jupiter Notebook links made me freeze. But this intensity of challenge brings me an improvement for my skills.

Thanks Coursera & IBM

创建者 konutech

•Dec 13, 2020

The instructor from videos is amazing. Great tutor. So far the courses from IBM Machine Learning Professional Certificate are really, really good.

创建者 Nandana A

•Dec 28, 2020

Learned really about supervised learning and more importantly regularization and some available methods.

创建者 Ranjith P

•Apr 13, 2021

I recommend this course to everyone who wants to excel in Machine Learning. This is a Great Course!

创建者 Luis P S

•May 4, 2021

Excellent!!! I rather recommend the course for those who need to understand properly and fast!

创建者 Vivek O

•Apr 10, 2021

Very well presented. This is without doubt the best series for Machine Learning on Coursera.

创建者 Wissam Z

•Jun 6, 2021

best course ever I learned regression and polynomials in a professional way.

thank you

创建者 Saraswati P

•Aug 11, 2021

Well structured course. Concepts are explained clearly with hands on exercises.

创建者 Goh K L

•Jun 5, 2021

Please give the lecturer credit and include him as one of the instructors

创建者 Patrick B

•Jun 16, 2021

Great way learn about machine learning development of regression models

创建者 Juan M

•Jun 11, 2021

Very well structured course, the explanations were very clear.

创建者 My B

•Apr 14, 2021

A well structured course with useful techniques in real life.

创建者 Ana l D l

•Jul 21, 2021

like that it uses math and also use programming

创建者 george s

•Aug 20, 2021

Flawless course, everything was perfect!

创建者 Nikolas R W

•Dec 24, 2020

Great course to learn about regression!