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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
4,273 個評分
742 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

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

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!!

篩選依據:

151 - Applied Machine Learning in Python 的 175 個評論(共 723 個)

創建者 Steven L

Apr 08, 2018

Very practical introduction to using Python for machine learning - less focused on theory and more focused on how to use the sklearn library and proper use cases for different classifiers and regressors.

創建者 Michael D

Jul 19, 2017

I thought this was a fascinating course that tried to do the near impossible and succinctly summarise the key techniques of machine learning. And it did that very well. Very challenging tasks, but also overall inspiring for the next step.

創建者 Licheng Z

Jul 09, 2017

This is a great course

創建者 Vladimir

Sep 27, 2017

A course that gives not only solid understanding of Machine Learning, but provides with skills to actually practice it on real world datasets. Highly recommended.

創建者 Arnab

Nov 03, 2017

Excellent

創建者 ruchisahu

Aug 10, 2017

deep yet simple to understand.

創建者 Joan P

Nov 05, 2017

Very interesting last programming assignment

創建者 Nathan R

Oct 17, 2017

Excellent course. A practical application of the concepts in Python/sklean.

創建者 Evgeny V

Apr 04, 2018

Excellent course!

創建者 Tianyang Z

Jul 06, 2017

great course. Learned a lot

創建者 Praneet N

Jan 09, 2018

i love the course

創建者 Armand L

Apr 24, 2018

Very Good Course ! learned a lot !

創建者 Petko S

Apr 03, 2018

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!

創建者 Marek S

Sep 29, 2017

Useful, practical use of sklearn to machine learning tasks

創建者 Lucas G

Jun 05, 2017

Great course! Really appreciated it, it taught me (and gave me lots of practice) how to use lots of different classifiers for machine learning.

創建者 Emanuele P

Oct 25, 2017

It gives you the methods and the essential knowledge to build a learning pipeline using Python and SciKit-learn tools.

創建者 Aviv N

Jul 16, 2017

great course

創建者 Maciej W

Jul 08, 2018

Very informative, broad, hands-on course. Strong recommend.

創建者 David V

Jul 28, 2017

Excellent course!

Machine Learning is today a buzzword and you do not really know what it is until you do it. The University of Michigan has put together a great program that takes you from the basics of Python to the latest Machine Learning techniques.

I started without knowing Python, and well, I cannot say that it has always been easy, but I DID IT!

創建者 Shashi M

Sep 25, 2017

Very good course for a wide spectrum of audience interested in Machine Learning. I just had a basic learning of ML and Python, but the course was structured so well that I could catch-up. Also offers an interesting peak into Neural Networks and Deep learning. Overall, an excellent course with clear and attainable objectives, backed by high quality content and data.

創建者 Sajit K

Jun 26, 2017

Insightful and hands on course

創建者 Zihao H

Mar 06, 2018

Extremely useful courses, and well taught lectures, and reasonable assignments

創建者 Dan S

Dec 30, 2017

Fantastic Course. I highly recommend it.

創建者 Jesus P I

Apr 18, 2018

The most practical course I have completed so far. Also the right amount of theory needed to being able to start resolving your first machine learning problems. 100% recommendable

創建者 Kevin L

Jun 25, 2017

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course. It covers a *lot* of material and the pacing is *very* fast. Week 2 is particularly long, and if you are still a student/working it may take an extra week to complete the course. Quizzes and assignments are not terribly difficult, but be careful of the project assignment in Week 4 (though the bar for a 100% is quite low!). Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.

A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases. The instructor mentions things to be covered "later," but that "later" never comes (for example, in discussing Grid Search). For some background, this course appears to have been repeatedly delayed before its release. To me, is understandable that the creators wanted to get this course out given the demand, but the rush is felt.

Ultimately, however, this is still an excellent introduction to Python Machine Learning, and I do feel the course is well worth taking. Just be prepared to do some more individual learning; however, shouldn't one always be for an online class?)