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Вернуться к Supervised Machine Learning: Regression

Отзывы учащихся о курсе Supervised Machine Learning: Regression от партнера IBM Skills Network

4.7
звезд
Оценки: 345

О курсе

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....

Лучшие рецензии

MM

21 сент. 2022 г.

This course is very helpful. The wonderfull part in this course was the final course project in which I had to create my own linear regression model by adding polynimial features and regularization.

GP

23 нояб. 2022 г.

Great Course curated by IBM team. It is really designed well and helps to achieve the goal. It is as per the industry standard, and practical. One can do this course thoroughly and get a job.

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1–25 из 76 отзывов о курсе Supervised Machine Learning: Regression

автор: Christopher W

25 янв. 2021 г.

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.

автор: Nick V

16 нояб. 2020 г.

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

автор: Abdillah F

7 нояб. 2020 г.

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

автор: Nandana A

28 дек. 2020 г.

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

автор: Ranjith P

13 апр. 2021 г.

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

автор: Minh L

30 сент. 2021 г.

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

автор: Nir C

8 окт. 2021 г.

Great course! Covered everything I wished to learn!

автор: Nancy C (

24 апр. 2021 г.

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.

автор: michiel b

15 февр. 2021 г.

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.

автор: Kalliope S

24 июня 2021 г.

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.

автор: Ramesh B

30 янв. 2021 г.

The course is incomplete on regression analysis. Also, the grading scale was biased after putting in a lot of time and effort(20 pages). The reason was I didn't follow the assignment questions.

автор: Eduardo P L

19 июля 2022 г.

Really difficult. Exams are not fully fair. Example: First exam in Week 3 - including videos 1 to 4. There is one question which answer is in video 8. And so many examples like this one.

Slides in videos are not provided.

автор: Weishi W

6 февр. 2022 г.

It is actually disguisting course. Simply reading the powerpoint without any clear explanation. So bad

автор: S. H M

5 янв. 2023 г.

I have seen various courses on machine learning and linear regression. This course has been one of the best courses in this field. It provides great detail in the theory and addresses important issues in the field regarding features engineering, regularization, and Ridge, LASSO, and Elastic net models. It also has great practice labs.

автор: Minhaj A A

22 сент. 2021 г.

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.

автор: K T V N S S K

3 авг. 2022 г.

this course fantastically awesome and we can learn machine learning i this course upto the core knowledgr with the help of this course i would strongly recommend you to join this course to gain knowledge rearding machine learning

автор: serkan m

3 мая 2021 г.

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.

автор: Nicola R

22 февр. 2022 г.

Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences. A good understanding of basic regressors is gained from this course.

автор: MAURICIO C

25 мар. 2021 г.

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

автор: Mahateer M

22 сент. 2022 г.

This course is very helpful. The wonderfull part in this course was the final course project in which I had to create my own linear regression model by adding polynimial features and regularization.

автор: Gopi P

24 нояб. 2022 г.

Great Course curated by IBM team. It is really designed well and helps to achieve the goal. It is as per the industry standard, and practical. One can do this course thoroughly and get a job.

автор: Alparslan T

6 янв. 2022 г.

Linear Regression, Ridge, Lasso, Elastic Net, L1 and L2 regularizations... All very well explained theoretically and coded on Jupyter Notebook accordingly.

автор: konutek

13 дек. 2020 г.

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

автор: Mohammad K K

12 авг. 2022 г.

It was a great learning experience with in-depth knowledge and practice-based demos helped me to understand the concepts easily.

автор: Mustafa B

19 сент. 2022 г.

It is very successful in explaining the topics and showing them in practice. thanks IBM and coursera