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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
4,378件の評価
758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: 51 - 75 / 740 レビュー

by Weinan H

Jan 21, 2019

A very systematic introduction to most used machine learning models.

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 Xiaoming Z

Jan 11, 2019

Very informative, useful practice

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 Tom M

Jan 23, 2019

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

by Manik S

Feb 08, 2019

Optional references to the inner workings should be provided. For example how Decision Trees are trained and how the best division is decided.

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 Shaukat

Feb 11, 2019

excellent course

by Pieter J V V V

Feb 13, 2019

Inspirational course, learning you in a comprehensive manner, a thorough approach to machine learning with the target specific peculiarities and possible pitfalls.

by Anne E

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

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 Varga I K

Feb 25, 2019

Great and Strong fundamentals on machine learning without too much mathematics involved in it.

by Angadvir S P

Feb 24, 2019

The course was very useful, however, few of the assignments (specifically assignment 2) had a few errors in accurately displaying the question content and grading method was found to be slightly inconsistent with what was asked in the cells (Jupyter notebook).

4.5/5.0 stars

by Naman M

Feb 26, 2019

The Instructor is marvelous. The Assignments are amazing, The TA is really responsive. The content only for one month course was outstanding, my feedback would be to increase the amount of exercises(coding) and assignments, and make the course for 2 months.

by Purna C K

Feb 21, 2019

It's a superb course well organised with good and real time examples.

by AMAN K

Mar 06, 2019

Course Material is quite interesting and practical.

by James S

Feb 21, 2019

Very excellent course. Well done explanations even if there is some language confusion. Taking the time to really understand the concepts makes all the difference.

by Esmerlin R M

Mar 04, 2019

Este curso es increíble.

by Muhammad A a

Mar 06, 2019

Great course

by Fabiano R B

Mar 08, 2019

The course is a great overview of the basic algorithms that every machine learning practitioner should know. Since it has a limited amount weeks to cover such a broad subject, you will have to dig a little deeper by yourself. I found the reading material also very interesting. The final project is awesome and it will definitely make you experiment what is exactly what a Data Scientist should do.

by miguel c

Mar 10, 2019

Great collection of applied Data Science concepts, worked examples and challenges using python

by Andrew

Mar 11, 2019

Really well explained theory without too much of a mathematical deep dive that provides a perfect set up to learn about machine learning from a purely math/stats perspective through Andrew Ng's Machine Learning course or self study

by Harsh S

Mar 10, 2019

Great content

by Oliver O

Mar 11, 2019

Great course!