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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4.6
4,240 個評分
738 個審閱

課程概述

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....

熱門審閱

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

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226 - Applied Machine Learning in Python 的 250 個評論(共 720 個)

創建者 Marc B

Jun 30, 2017

I learnt a lot about machine learning - great assignments as well

創建者 Danish R

Jul 02, 2017

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

創建者 Gerardo M C

Oct 12, 2017

Great course!!

創建者 Francisco M

Jul 12, 2017

Good stuff here

創建者 Sashi B

Jul 31, 2017

One of the best courses I have taken online! The professor lectures are great and very well laid out. The assignments are very challenging and meant to teach you real life scenarios. Highly recommend to anyone who wants to learn the basics of machine learning using Python.

創建者 Gustavo W

Jun 19, 2017

Great course! Introduces ML algorithms using python's scikit-learn in an easy to follow way

創建者 lvbart

Apr 30, 2018

this course may be the most challenging one I have ever met, those concepts and examples I have never thought would met in my life. but after intense learning and excellent course arrangement, I may get a little sense of machine learning now.

Thanks for the great job, dear applied machine learning in Python team!

創建者 Ana C R

Aug 31, 2017

Very practical view, while still containing a lot of good theoretical content.

創建者 Martyna S

Nov 16, 2017

Very interesting and engaging course. I liked graphical comparisons of different models and their params. Module notebooks were very handy while doing assignments. All homeworks were not trivial, developing and demand attention to detail. Big plus for teachers posts at forum - they help a lot while doing quizzes and assignments.

創建者 Markus T

Aug 12, 2017

Ist gut

創建者 Michael B

Jun 19, 2017

Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.

創建者 Holger P

Oct 07, 2017

Great course covering Python's Machine Learning library scikit-learn.

創建者 Jeroen D

Jun 14, 2018

Good introduction into the scikit learn package, took way more time than advertised but I also learned more than expected.I contrast to course 1, the assignments were easier, but the quizes were harder. Distribution of materials could have been better: week 2 has by far the most material to digest and learn.

創建者 Valeriya P

Jul 25, 2017

multi selection questions in quizzes are a bit hard to handle, i think there should be more hands-on experience included. Loved notebooks when one answer lead to the next one.

創建者 Limber

Dec 03, 2017

It is a very practical course if you have learned the Andrew Ng's Machine Learning course. It is much much more practical and I have gained a lot from it. I really wish I could learn it soon. Thanks very much.

創建者 Qian H

Jul 05, 2017

Nice course with an good level. I really leant a lot from this course!

創建者 Shao Y ( H

Sep 08, 2017

Very good survey of all fundamental topics of machine learning! Good resources for preparation for technical data science interview! :)

創建者 Mohamed H

Jun 26, 2018

C'est le meilleure cours en pratique que j'ai rencontré dans toute ma vie.je vous remercie énormément pour m'offrir cette cours et je remercié mon professeur pour la simplicité et la méthode avec laquelle a fait ce cours.

創建者 Rahul N P

Jun 15, 2018

Thank you Kevyn!

創建者 Luiz H Q L

Jun 26, 2017

Excellent course.

創建者 Vibhore G

Feb 09, 2018

From this course you will learn direct application of Machine Learning using python. You can dive into the world of machine learning. Ipython notebooks used are really helpful. Learned a lot from this course.

創建者 Li T

Oct 28, 2017

Informative course. Five stars!

創建者 SRIHARI

Jul 18, 2017

This is good course gives in depth information.

創建者 Kuntal B

Oct 25, 2017

Awesome !!!

創建者 Jeroen v L

Jul 24, 2017

I like how the many different ML algorithms are explained in such a high-level. This is a good anti-dote to more theoretical courses. Best MOOC I have done so far.