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学生对 密歇根大学 提供的 Applied Machine Learning in Python 的评价和反馈

4.6
4,230 个评分
734 个审阅

课程概述

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

热门审阅

OA

Sep 09, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

FL

Oct 14, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

筛选依据:

251 - Applied Machine Learning in Python 的 275 个评论(共 720 个)

创建者 Daniel N

Jul 10, 2017

I think this course is a real challenge and gives a great introduction to machine learning. I enjoyed it

thoroughly even if I had my troubles with the Quiz questions.. Great course overall, I would recommend it to anyone.

创建者 Fabio C

Jun 22, 2017

The course is well done and both the lectures and the practical assignments have generally a high quality. If you come from a theoretical background, be aware that this is a very "high level" course, meaning that a lot of attention is put on the practical application of the different ML methods (using the sci-kit learn library in python), but very little is said about their mathematical foundations.

创建者 Γεώργιος Κ

Jan 11, 2018

A must to to have lesson for Data Science using Pandas and Matplotlib

创建者 Ruyang L

Apr 21, 2018

Very interesting course, enjoyed it very much

创建者 Harshit K

Oct 05, 2017

One of the great courses to learn machine learning in Python.

创建者 Dheeraj P

Aug 24, 2017

nice lecture series, Good Approach .

创建者 Yan

Jul 05, 2017

100% Free course as audit, recommend

创建者 Lawrence O

Jun 29, 2017

Very informative about machine learning approaches ie supervised and unsupervised learning. And then goes into detail about the techniques such as regression and classification for supervised learning and clustering (K-Means) for unsupervised learning. Other techniques are discussed such as Principal Component Analysis etc.

I enjoyed it and would recommend for all data enthusiast.

创建者 Aniket B

Jun 24, 2017

Awesome course

创建者 Jose S

Jun 16, 2017

Great course. The material is well thought, the assignments are excellent. I learned a lot and I am already leveraging what I learned in the course at work.

创建者 Saiapin A

Jul 24, 2017

This is a great course for those who want to get acquainted with machine learning basics as well as its applications.

创建者 Nitin P

Feb 28, 2018

Very Interesting and fascinating Course of Machine Learning

创建者 xixicy

Apr 10, 2018

The content (slides, python scripts) is very structured. The lecturer explained very clearly. The reference articles were super inspiring. Also, the assignment is very well designed and relevant to what's covered (in comparison, some other courses might have very difficult assignments which need much more self-learning and cause frustration). Thank you!!

创建者 Sayan G

Jun 15, 2018

Exhaustive and in depth coverage

创建者 Oleh Z

Feb 27, 2018

G

创建者 Mykhailo L

Jan 06, 2018

Great course with excellent homework assignments

创建者 Guneet B

Apr 02, 2018

High Quality resources and materials

创建者 Rob N

Oct 15, 2017

This course was challenging and extremely interesting. The long and detailed lectures and excellent lecture notes covered the material very thoroughly for an online course.

创建者 Hari S R V

May 30, 2018

Great course

创建者 Darío A

Jun 03, 2018

Excellent course to get into sci kit leran

创建者 Saifullah

Jun 08, 2018

Well designed for practicing, helps a lot in applying ML in python

创建者 Ranabir G

Jun 04, 2018

Assignments are good

创建者 Mostafa A A

Sep 23, 2017

This is the most useful machine learning course in the internet. It helped me to understand machine learning algorithms very well that I never saw in other courses. This course covers most of the machine learning algorithms that needed nowadays. Thanks to Michigan University and Coursera to make this course to be available online.

创建者 Anad K

Mar 28, 2018

Excellent course for Machine Leaning. Discusses wide range of Supervised machine learning and gives a very brief introduction on Clustering algorithms(Unsupervised). Users can immediately put to use the knowledge gained during the course.

Some more briefing about feature transformation and other such elements can be included in the course material to make it better. Also unsupervised machine learning could have been included with grater depth. Overall this course is highly recommended to aspirants interested in ML with some python knowledge.

创建者 Чижов В Б

Nov 15, 2017

Very interesting and informative! The material outlined in the course, difficult to understand, IMHO, but the organizers and the teacher managed to present it in an accessible form. Special thanks to Kevyn Collins-Thompson for his lectures and Sophie Grenier for her work and attention to the forum.