Chevron Left
返回到 Applied Machine Learning in Python

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

4.6
5,934 个评分
1,056 条评论

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

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!!

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

筛选依据:

901 - Applied Machine Learning in Python 的 925 个评论(共 1,042 个)

创建者 Niv B

Jul 30, 2018

On 1x speed, I'd rate it 3 stars, on 1.5x its 4.

The professor just speaks too slow.

创建者 Claire-Isabelle C

Jun 24, 2017

I learned A LOT in this course and was pretty proud to pass all the assignments.

创建者 SAYANTAN B

May 09, 2020

Very nice and informative course..Keep it up. This course has helped me a lot.

创建者 Hanchi W

May 18, 2019

Good content, some coding assignments are hard to submit(csv file not found)

创建者 Bharat R

Aug 22, 2017

Nice course. Multiple choice quizzes could have been worded a bit better.

创建者 Grace Y

May 15, 2020

the material for self-learning after classes is not comprehensive enough.

创建者 Douglas P

May 28, 2018

Generally worth while but the automatic grading system could be improved.

创建者 Daniel A

Sep 01, 2018

Very useful. It's the right course to take after Andrew Ng ML course.

创建者 Shwetank A

Jul 23, 2019

Algorithim are not explained much better, just coding is explained.

创建者 Hardik A

Jan 04, 2018

An amazing course for learning the application of machine learning.

创建者 Tom M

Sep 28, 2017

Clean programming examples. A little simplistic for advanced users.

创建者 Davide M

Oct 24, 2018

Should be an harder final assignement, but a great course overral!

创建者 Jeffrey D B

Oct 16, 2018

Pretty good class, decent but very quick walk-through of ML tools.

创建者 Manuela D

Aug 08, 2019

Well organised, lots of details, a good overview of ML algorithms

创建者 Sang L

Jul 28, 2018

Speed kinda fast but maganeable. Need more detiailed notes/slides

创建者 vatsal m

May 26, 2020

Some of the assignments have bugs in them please rectify them.

创建者 Anuhya D

Oct 14, 2019

pre-processing and unsupervised learning needs more emphasis

创建者 Deleted A

Jul 13, 2018

there are some gaps which is really difficult to understand!

创建者 Xingyu W

Oct 14, 2019

Need a better configuration for homework data file loading.

创建者 DENIS R

May 23, 2020

allowed me to hone my knowledge of machine learning models

创建者 Jason A

Jun 26, 2018

This course was tougher than expected, but I learned a lot

创建者 Bernardo A

Jun 08, 2017

Great content and good assignments! Learned a lot from it.

创建者 Venkata S M B

Jun 02, 2020

Decent course. I'd call this, 'Intro to Machine Learning'

创建者 Wang Y

Feb 16, 2018

Good, despite some confusions in the lecture and quiz.

创建者 Tangudu S S

May 23, 2020

Got a very clear picture of ML usage in Data Science.