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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
4,343 个评分
753 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:

151 - Applied Machine Learning in Python 的 175 个评论(共 735 个)

创建者 Thomas W

Mar 06, 2018

g

创建者 Manuel T

Jan 14, 2018

very good course: fast-paced, very straight-forward explained. Assignments do make sense and repeat what you learn during the course. I can recommend it

创建者 David A d A S

Jul 31, 2017

Awesome.

I learned a lot of fundamentals machine learning. The lectures are very clear and the assignaments focus on practical examples.

I recomend this course for everyone who want to have a global view of machine learning.

I enjoyed a lot.

创建者 Darren

Jul 02, 2017

Very Impressive and illustrative !!

创建者 Daniel C C

Jun 22, 2017

Amazing!!

创建者 Paul M S

Jul 31, 2017

Very informative and educational

创建者 Martin H

Jul 06, 2017

very excellent course, must take if you are welling to deal with data and applying ML al. to it

创建者 Ebenezer A W

Nov 15, 2017

A really nice course to begin machine learning with

创建者 Jephter K P

Sep 05, 2017

This was an opportunity i could never miss, i have learnt a lot

创建者 刘宇轩

Dec 14, 2017

The last homework is great and interesting.

创建者 Nattapon S

Aug 03, 2017

It is a good class. I learn a lot from this course. It is a concise starting course for Python machine learning. I recommended this course.

创建者 Jim S

Jul 02, 2017

Excellent content and delivery.

创建者 Jakob P

Sep 02, 2017

Fundamental, but still thorough, course in applied machine learning using Python. The lecturer is really good, and the quiz/problem sessions are challenging, but sufficient information is provided in the videos -- a HUGE improvement compared with the first two courses in this specialization.

创建者 Srilatha T

Jan 09, 2018

excellent

创建者 Artur A

Aug 04, 2017

Best introduction to sklearn library I came across!

创建者 Pooja C

Jun 17, 2018

Helped me understand the fundamental concepts and practice them with assignemens. I highly recommend this course.

创建者 Matt R C

Feb 15, 2018

The course was very well prepared and the instructor presented the material clearly and informatively. I've seen some courses where you spend more time trying to understand and keep up with the instructor. In this instance, this was not the case and you could spend more time understanding the material. The instructor spoke slowly and clearly.

I do have to say I purchased the corresponding book as recommended but I didn't feel it was necessary. Good book, I just think the material in the course was presented well enough on its own.

创建者 Dipanjan S

Jun 24, 2017

Excellent clarity, recommended for getting started with ML

创建者 Piotr B

Jun 01, 2017

a

创建者 Refik E

Sep 20, 2017

I thank Dr. Kevyn Collins-Thompson and Coursera team for the excellent course. I have learned valuable skills from the course. Dr. Thompson explained ML concepts very skillfully and made the course fun to follow. Assignments are very well selected and reinforce the class concepts. Over-all the course encourages learner to investigate and apply different ways to do same task. I recommend this course to those who are willing to learn machine learning and can't decide where to start.

创建者 陈熠

Aug 13, 2017

Very good machine learning course working on python with little mathematics.

创建者 Dongliang Z

Dec 22, 2017

Very good lecture for beginner:easy to understand.

Also good assignment: force you to use what you learned in the course.

The discussion forum is helpful when you meet difficulties in assignments and quiz.

创建者 Peter D

Nov 06, 2017

Nice pragmatic approach how to apply machine learning. Compelling examples, datasets and useful tips how to visualise features.

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 Dylan E

May 03, 2018

I enjoyed this course it was fun and very informative. This course also gave me a bunch of resources such as The Elements of Statistical Learning which is a great book!