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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

7,973 个评分
1,452 条评论


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....



Oct 13, 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!!


Sep 8, 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


701 - Applied Machine Learning in Python 的 725 个评论(共 1,442 个)

创建者 Dario M

Jul 12, 2019

So far the best course in this specialization

创建者 Rohit M S

Mar 22, 2019

The Course is amazing. you get to learn a lot

创建者 Xiaoyue Z

Jul 30, 2018

A very helpful and confidence-building class!

创建者 Ruyang L

Apr 20, 2018

Very interesting course, enjoyed it very much

创建者 zios s

Nov 23, 2017

great course very useful in data science job.

创建者 Om P

May 17, 2020

perfect for beginners! thank you, professor!

创建者 Pilar V

Sep 14, 2019

Super interesting course and specialization!

创建者 Joan P

Nov 5, 2017

Very interesting last programming assignment

创建者 David M

Jul 7, 2017

Great introduction to Scikit-learn tool set.

创建者 Danish R

Jul 2, 2017

P.S.: This is not an easy course to complete

创建者 Amey k

Jan 9, 2022

best course for machine learning enthusiast

创建者 sudipta d

Oct 29, 2021

this course helps me to building my skills.

创建者 roberto T

Aug 17, 2020

Good course, especially on the applied side

创建者 Ranjit K

Jul 26, 2020

Great Learning with good examples and tasks

创建者 Olivier R

Jul 1, 2020

Highly Recommended, the Instructor is great

创建者 刘宇轩

Dec 14, 2017

The last homework is great and interesting.

创建者 Thodoris N P

Oct 26, 2017

Most complete Machine learning course ever.


Nov 30, 2020

very organized and helpful course. Thanks!

创建者 Anurag B

Jun 8, 2019

Great Content, Great Delivery, Thumbs Up!!

创建者 Darío A

Jun 2, 2018

Excellent course to get into sci kit leran

创建者 Drew O

Oct 8, 2017

Great course. Challenging and informative.

创建者 Mohsen

Aug 3, 2017

I've learned a lot. Very practical course!

创建者 Ayush R

Nov 9, 2020

very well details of concept and learning

创建者 Puran Z

Jun 1, 2020

Great course. I love it, thank professor.

创建者 MOH S

May 19, 2020

Excellent content and perfect instructor.