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Applied Machine Learning in Python に戻る

ミシガン大学(University of Michigan) による Applied Machine Learning in Python の受講者のレビューおよびフィードバック

4.6
4,365件の評価
757件のレビュー

コースについて

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....

人気のレビュー

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

フィルター:

Applied Machine Learning in Python: 251 - 275 / 739 レビュー

by Manoj K K M

Jun 30, 2018

For applied machine learning, outstanding. It could be improved with bit more theory, which gives more insight to the concept.

by DESHPANDE J S

Jul 10, 2017

I am a beginner in Machine Learning. I find this course very easy to follow, interesting and informative. Thank you for the efforts you've put in!

by Oscar J O R

Jul 01, 2017

Amazing module. Clear explanations and useful examples and exercises.

by Sourabh J

Jun 21, 2017

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

by Fernanda R L

Oct 09, 2017

Very good, beyond my expectations

by Dongsoo J K

Jul 18, 2017

Very good and straightforward

by 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.

by Guenael S

Jan 20, 2018

The class provides a perfect introduction to the scikit-learn Python module. The videos are engaging and insightful. The quizzes are challenging while not requiring too much time writing out solutions (it does take time finding some of the more subtle answers, by reviewing details in the videos). The executable modules are perfect to bootstrap machine learning projects. Homework assignments can get complicated, and you should be familiar with advanced data structure manipulation in pandas and numpy to make progress. Assignment grading is very well done.

by John B

Mar 18, 2018

Challenging but worthwhile mix of essential theory (explained well) and hand-on practice with good, sensible exercises to help one get a confident grasp of scikit learn packages which one can use in the real world. Many thanks to the organisers and Coursera.

by Ji W P

Jul 23, 2017

excellent course. Lectures were good, not too heavy on theory, and assignments were challenging but doable. I liked assignments much better than the quizzes

by Holger P

Oct 07, 2017

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

by Megha T

Jun 26, 2017

helpful !

by Matthias B

Aug 05, 2017

Great course, very hands-on. Maybe difficult to follow without any prior knowledge in machine learning, though.

by Jim S

Jul 02, 2017

Excellent content and delivery.

by 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.

by Anthony K

Jul 05, 2017

So far the course is relevant and very approachable.

by Daniel C C

Jun 22, 2017

Amazing!!

by Paul M S

Jul 31, 2017

Very informative and educational

by Arun S

Nov 09, 2017

Great professor with lot of real world experience.

by Sridhar I

Dec 21, 2017

A great crash course in some of the basics of machine learning on Python. Although not explicitly covered, the assignments helped me gain an understanding on the Jupyter framework & pandas.

The final assignment was definitely a cherry on top that let me gain a very vivid insight into the field.

by Marcin C

Apr 29, 2018

Heavy, but extremely valuable course

by Piotr B

Jun 01, 2017

a

by Christos G

Sep 01, 2017

Following the first 2 sessions of this specialisation, this one seems easy and gives the student a lot of confidence. Make sure you follow the sequence suggested in this specialization, even if you do not plan to continue with Text Mining and Social Networks.

by Ali A

Mar 22, 2018

Excellent!!!

by Baskaran V

Dec 30, 2017

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