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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4,814 個評分
839 條評論


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....



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


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!!


351 - Applied Machine Learning in Python 的 375 個評論(共 821 個)


Sep 18, 2019

Course was fully explained in details and with good exercise

創建者 Ayon B

Oct 19, 2018

Good course. And challenging indeed, especially the quizzes.

創建者 Sathvik K

Aug 28, 2018

great for learning how to practically apply machine learning

創建者 Sunil S

Jul 05, 2018

Great course for implementing machine learning using python.

創建者 Mikhailov R

Jan 27, 2019

Sometimes the lecturer is boring but overall perfect course

創建者 Maciej W

Jul 08, 2018

Very informative, broad, hands-on course. Strong recommend.

創建者 Nitin P

Feb 28, 2018

Very Interesting and fascinating Course of Machine Learning

創建者 Biju S

Oct 12, 2017

Very tough to finish. Big gap with material and assignments

創建者 Amoghavarsha B

Mar 19, 2020

Perfect course with lots of assignments and good material!

創建者 Yu S

Jul 16, 2018

Good applied material to study along theoretical material!

創建者 Marek S

Sep 29, 2017

Useful, practical use of sklearn to machine learning tasks

創建者 Matias B M

Aug 15, 2017

Challenging and rewarding. Wouldn't have it any other way.

創建者 Dipanjan S

Jun 24, 2017

Excellent clarity, recommended for getting started with ML

創建者 Sung C

Sep 25, 2017

Very well organized and useful for hands-on application.

創建者 Ankush G

Jan 14, 2020

A good stepping stone towards a career in data science.

創建者 Fei F W

Nov 07, 2019

The course is very well structured, highly recommended!

創建者 Ajay S

Jan 29, 2019

great course thanks for financial aid for the course .

創建者 Kristin A

Jan 13, 2019

Great intro to the tools of machine learning in Python

創建者 Walt M

Jul 29, 2018

Good class. The asignements made me a better engineer.

創建者 Alonso S A

Nov 10, 2017

Very usefull, easy to understand and full of examples.

創建者 Dongxiao H

Jan 31, 2018

It is helpful for me to be familiar with scikit-learn

創建者 Tue V

Mar 25, 2020

I have learnt a lot from this course. Thanks so much

創建者 Joshua A

Dec 03, 2019

An excellent overview of Machine Learning in Python.

創建者 Jose Á P L

Mar 17, 2019

Muy buen curso para iniciarse en el machine learning

創建者 Dibyendu C

Oct 17, 2018

Well structured and quality lectures and assignments