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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
4,338 個評分
752 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 735 個)

創建者 Julian O

Jul 05, 2018

Great overview

創建者 Lawrence O

Jun 29, 2017

Very informative about machine learning approaches ie supervised and unsupervised learning. And then goes into detail about the techniques such as regression and classification for supervised learning and clustering (K-Means) for unsupervised learning. Other techniques are discussed such as Principal Component Analysis etc.

I enjoyed it and would recommend for all data enthusiast.

創建者 Nan L

Jun 18, 2018

I think this course is difficult for me

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 Eduard M

Jul 05, 2017

The evaluation is sometimes problematic, otherwise is very good. I had a problem with matplotlib which was just imported and nowhere was written to remove it.

創建者 Frank L

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!!

創建者 Lam M

Jun 09, 2017

Very well designed courses! There are many materials to go in depth even if you have done Python Machine Learning in the past.

創建者 Ana C R

Aug 31, 2017

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

創建者 Dongxiao H

Jan 31, 2018

It is helpful for me to be familiar with scikit-learn

創建者 Obed I R

Jul 20, 2017

Fantastic, and challenging course, a must and recommended. For those who are interested in Machine Learning and have experience with Pandas and Python programming.

創建者 Mauritz v d W

Sep 24, 2017

One of the best applied machine learning courses I have come across.

創建者 shubham

Mar 17, 2018

great

創建者 Cristinel M

Jul 16, 2017

Great value! An excellent complement of Andrew Ng's course on Machine Learning!

創建者 Iver B

May 02, 2018

An ambitious but systematic overview of a wide range of machine learning techniques using scikit-learn and other Python libraries. Prof. Collins-Thompson is a steady and clear explainer of somewhat complex topics. The exercises and quizzes can be challenging, but are very worthwhile.

Overall, very well done.

創建者 Lorena G

Aug 04, 2017

Still some hard for me both the quiz and assignment, however it is interesting and worth studying

創建者 RIAHI L

Oct 19, 2017

very good course, I enjoyed it

創建者 Alejandro R

Jul 08, 2018

Good choice for Machine Learning introduction, Data Analysis in Python and applied statistical concepts.

創建者 abdulkader h

Jul 04, 2017

I appreciate so much this course even it was so dense and slitly short. It would be useful to extend it over several weeks again.

創建者 Daniel N

Jul 10, 2017

I think this course is a real challenge and gives a great introduction to machine learning. I enjoyed it

thoroughly even if I had my troubles with the Quiz questions.. Great course overall, I would recommend it to anyone.

創建者 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.

創建者 Drew O

Oct 08, 2017

Great course. Challenging and informative.

創建者 Alan J

Jul 02, 2017

This was an awesome and engaging course. Machine Learning is a vast field with lots of ground to cover. This course gives a broad overview of all the different parts of machine learning without going too deep and also keeping everyone engaged. The assignments, especially the last one test what you learned and keeps you on your toes. A good beginner course to Machine Learning. Thank You!

創建者 Saurav B

Dec 24, 2017

Good Course

創建者 Shuyi Y

Jun 28, 2017

This course is great because I received so much training in applying the ML packages and functions python. A lot of hands-on experience!