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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
4,338件の評価
752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: 26 - 50 / 735 レビュー

by Michael T B

Dec 19, 2018

Great class! I had fun learning many new things in this course. The professor did a very good job at taking a complex subject and making it simple and easy to understand. The code and assignments were straightforward and not overly difficult. The real quizzes/tests in this course were appreciated as this felt more like a "real class" where one can really learn a lot. One of the best online classes that I have taken.

by Angelo S

Dec 21, 2018

An excellent resource to immerse yourself into machine learning methods. Professor Kevyn explains key concepts in the most intuitive way possible. It does require some previous experience in Python.

by Shishir N

Jan 09, 2019

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by Mehmet F C

Dec 27, 2018

good one to quickly start learning ML - covering models, what they do, and how to tune them. Not going deep into the "how" models work.

by SURENDRA O

Dec 25, 2018

The course was very well designed. The pace of the lectures are perfect unlike other course when the instructor moves very fast.

by Phillip L C

Dec 25, 2018

Great course - balanced and very revealing for direct application.

by John D L C

Dec 27, 2018

This is an excellent course.

by Nithin R

Dec 25, 2018

contents are good.

by Henryk S

Dec 28, 2018

I have been confidently guided through the complexities of Machine Learning through perfect mix of lectures and reading materials. Quizes and programming assignments served as very helpful tool to zoom in on specific details which in further assignments will make the difference between success and failure.

by Rajendra S

Jan 11, 2019

This course is the one that I enjoyed most while learning anything in Coursera. Thank you everyone associated with this course and content.

by Martin U

Jan 11, 2019

Tough class, learned not to give up and keep trying. Even went back and redid some quizzes in order to get a better grade.

by Xiaoming Z

Jan 11, 2019

Very informative, useful practice

by Lewis M

Jan 13, 2019

Very good course for either an introduction to machine learning or to refresh old skills. It's also very good at putting emphasis on topics that data scientists may overlook / not pay much attention too, so having this as a reminder to look deeply into each algorithm and its application or limitations is incredibly helpful.

by Kristin A

Jan 13, 2019

Great intro to the tools of machine learning in Python

by Abdirahman A A

Jan 13, 2019

In depth course that covers a lot in a short amount of time. If you take some extra time to delve deeper into these topics, you can ensure a great overview of machine learning with python.

by Megan J

Dec 31, 2018

In depth understanding is required to complete the assignments. Challenging without being demanding.

by Mohit T

Jan 16, 2019

Truly enjoyed the course, especially the assignment in module 4. Course is different from other similar courses as it provides good hands on experience.

by Liu L

Jan 03, 2019

This course provides a good introduction to using python in machine learning. It helps me to get hands on it.

by Daniel H

Jan 04, 2019

Kevyn Collins-Thompson is a legend

by Fábio R D d B

Jan 17, 2019

Great course. Good mood to expose info. Congrats for content!

by Yingkai

Feb 15, 2019

It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken. Strongly recommend for your knowledge and career advance. Great professor!

by Tom M

Jan 23, 2019

Wow, great course, so much material covered. Will save this one for later review.

by Sumit M

Feb 19, 2019

This is a very good course about How to apply Machine Learning but I think before taking this course the student should take the Andrew Ng machine learning course by Stanford University to Learn the Important Mathematics behind the ML algorithms

But Enjoyed this course a lot

thank you

by Min L

Feb 06, 2019

A very good course to start journey on data science. Good combination of reading, lecture and practice.

by Darius T

Feb 19, 2019

Great course for learning how to apply Machine Learning algorithms with Python.