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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
4,247 个评分
739 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

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:

26 - Applied Machine Learning in Python 的 50 个评论(共 720 个)

创建者 Weinan H

Jan 21, 2019

A very systematic introduction to most used machine learning models.

创建者 Tom M

Jan 23, 2019

Wow, great course, so much material covered. Will save this one for later review.

创建者 Megan J

Dec 31, 2018

In depth understanding is required to complete the assignments. Challenging without being demanding.

创建者 Mohamed A H

Dec 15, 2018

Awesome course!

Stick till the end of it, and you'll never regret it.

You're gonna have a lot of fun especially in the last week, don't skip the optional readings of this week ;)

创建者 Samuel E G G

Dec 17, 2018

Fantastic. Though the teacher is not as good as the first one.

创建者 Liu L

Jan 03, 2019

This course provides a good introduction to using python in machine learning. It helps me to get hands on it.

创建者 Syam P N

Dec 17, 2018

Excellent course. Was very helpful

创建者 Daniel H

Jan 04, 2019

Kevyn Collins-Thompson is a legend

创建者 Olin S

Jan 06, 2019

The programming assignments where though because the automatic grader was very picky. Please change it so it gives the user more input about what part of their code is wrong. Also Have a repository where the user can retrieve previous submissions.

创建者 Dr S K

Jan 08, 2019

Good course. Lots of material, and direction what to study. I have really enjoyed it.

创建者 Shishir N

Jan 09, 2019

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创建者 Rajendra S

Jan 11, 2019

This course is the one that I enjoyed most while learning anything in Coursera. Thank you everyone associated with this course and content.

创建者 Xiaoming Z

Jan 11, 2019

Very informative, useful practice

创建者 Martin U

Jan 11, 2019

Tough class, learned not to give up and keep trying. Even went back and redid some quizzes in order to get a better grade.

创建者 Michael T

Feb 21, 2019

Great content and reference materials

创建者 James S

Feb 21, 2019

Very excellent course. Well done explanations even if there is some language confusion. Taking the time to really understand the concepts makes all the difference.

创建者 Purna C K

Feb 21, 2019

It's a superb course well organised with good and real time examples.

创建者 Shaukat

Feb 11, 2019

excellent course

创建者 Pieter J V V V

Feb 13, 2019

Inspirational course, learning you in a comprehensive manner, a thorough approach to machine learning with the target specific peculiarities and possible pitfalls.

创建者 Angadvir S P

Feb 24, 2019

The course was very useful, however, few of the assignments (specifically assignment 2) had a few errors in accurately displaying the question content and grading method was found to be slightly inconsistent with what was asked in the cells (Jupyter notebook).

4.5/5.0 stars

创建者 Varga I K

Feb 25, 2019

Great and Strong fundamentals on machine learning without too much mathematics involved in it.

创建者 Anne E

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

创建者 Yingkai

Feb 15, 2019

It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken. Strongly recommend for your knowledge and career advance. Great professor!

创建者 Naman M

Feb 26, 2019

The Instructor is marvelous. The Assignments are amazing, The TA is really responsive. The content only for one month course was outstanding, my feedback would be to increase the amount of exercises(coding) and assignments, and make the course for 2 months.

创建者 Akash G

Mar 03, 2019

awesome DS