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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
4,338 个评分
752 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:

201 - Applied Machine Learning in Python 的 225 个评论(共 735 个)

创建者 Tongsu P

Feb 08, 2018

Really challenging course!

创建者 Madalina-Mihaela B

Jul 18, 2017

Awesome course. Very practical!

创建者 Flavia A

Mar 11, 2018

Practical class to learn well-known models and scikit-learn. The practice tests are great to help you move from theory to practice.

创建者 Drew O

Oct 08, 2017

Great course. Challenging and informative.

创建者 Hemalatha N

Oct 24, 2017

Very informative & highly useful.

创建者 Ivan R

Aug 06, 2017

Great course that covers the key aspects of machine learning in a manner that is easy to follow

创建者 NoneLand

Jan 22, 2018

A very practical course for machine learning. By this course, one can get familiar with sklearn and pandas basic operation! The last assignment is a challenge for me. Thanks teacher for this great course!

创建者 Baskaran V

Dec 30, 2017

One of the very informative from the basic to intermediate course.

创建者 Ewa L

Jun 18, 2017

Fantastic course! Great foundation on scikit-learn. Really focused on APPLYING machine learning with just enough information about the models themselves to understand what's going on behind the scenes.

创建者 Amithabh S

Jun 23, 2017

Excellent course for someone who already has some knowledge of python but not quite familiar with machine learning. This course will teach you the application of machine learning in python.

创建者 Matt E

Aug 29, 2017

Learned a lot in this course! Much better than the previous two and also taught by a different professor.

创建者 KylinMountain

Jun 08, 2018

It's very impressive.

I suggest If we add a kaggle competition as a overall summery, that'll be great.

创建者 David C

Jun 26, 2017

This is a great course. Content is highly organized. The amount of lecture material was just about right. The professor is an excellent lecturer. Assignments and quizzes really helped reinforce my learning. If the Autograder is less demanding, this course would have been better in my opinion.

创建者 Sourabh J

Jun 21, 2017

Very Good and Relevant Course. Professor and TAs are very helpful.

创建者 erayonler

Sep 26, 2017

great course with a lot of hands-on experience

创建者 Krishna C P

Jul 04, 2017

excellent course for following reasons:

1. Excellent i python note books. What ever a student must know is kept in it.

2. every topic is explained simply and well upto what ever we need to know.

3. if you are not in academic field(not planning to do phd on this stuff). Trust me how ever advanced courses you do but after a week or month. these are the points which one need to remember.

4. Course and programming labs are in perfect sync.

Thank you very much for keeping this course

创建者 Fernanda R L

Oct 09, 2017

Very good, beyond my expectations

创建者 Dongsoo J K

Jul 18, 2017

Very good and straightforward

创建者 Zhu L

Oct 23, 2017

The course is very well-designed, with the first three weeks learning basic know-hows of all the tools we need, and the fourth week make full use of every model we've learned.

Even people with no prior CS background can get along well enough.

Getting 100/100 out of the final problem is actually a passing grade, very easy if you use what you've learned so far the right way.

When you're willing to spend more time exploring the models, methods and parameters, the reward will be worth your efforts.

创建者 Abdelrahman M s A

Feb 26, 2018

One of the best practical ML courses in the field!

创建者 Sung C

Sep 25, 2017

Very well organized and useful for hands-on application.

创建者 ShaoZhiyi

Jun 29, 2017

Pretty good. Could you add a new chapter for unsupervised learning?

创建者 Illia K

Dec 18, 2017

This course gave me some tools to use in real life. It's pretty abridged in time because they are trying to cover a very big topic in only 4 weeks. It won't give you a comprehensive set of knowleadges, but a good basis to proceed by yourself. Also some basic knowledges are reqired in computational mathematics, statistics and programming for applying this course. I highly recommend this course as a first step into machine learning.

创建者 clark x

Jul 02, 2017

very good

创建者 jay s

Jul 15, 2017

Excellent lectures, good exercises to reinforce the material, and absolutely loved the explanations of the sophisticated mathematical models that made them more lucid and easy to digest.