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Вернуться к Applied Machine Learning in Python

Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

4.6
звезд
Оценки: 5,295
Рецензии: 933

О курсе

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....

Лучшие рецензии

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!!

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251–275 из 926 отзывов о курсе Applied Machine Learning in Python

автор: 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.

автор: Shao Y ( H

Sep 08, 2017

Very good survey of all fundamental topics of machine learning! Good resources for preparation for technical data science interview! :)

автор: INHOI J

Apr 26, 2020

Great course. Professor delivered very complicated concepts of machine learning very easily. Quiz and assignments were very helpful.

автор: Quan S

May 08, 2019

Course materials are very systematic and instructive, and the professor teaches very clearly. I like this course and recommend it.

автор: 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.

автор: Émile J

May 19, 2020

The exercices and evaluations are more complex than in the previous courses in this short program, but also much more instructive.

автор: Himanshu B

May 15, 2020

It was really an excellent well designed course, I gained valuable information that I will use as a business analytics in future.

автор: Ivan S F

Mar 23, 2019

Very good course. Not very deep, but definitively very wide and appropriate for an overview course of machine learning in python.

автор: 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.

автор: Mahindra S R

Mar 28, 2020

Useful for understanding the application part of ML whereas Andrew Ng's course gives a more in-depth understanding of the topics

автор: 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.

автор: Ram N T

Jan 02, 2020

The course material and Professor Kevyn Collins-Thompson is awesome. A person who's seeking to learn ML should try this course.

автор: STEVEN V D

Jan 21, 2018

World class course.

Covers a lot of core machine learning subjects in an accessible way with a practical focus in Python.

Thanks!

автор: Peter D

Nov 06, 2017

Nice pragmatic approach how to apply machine learning. Compelling examples, datasets and useful tips how to visualise features.

автор: 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.

автор: SHRISH T

Aug 20, 2017

Very good course, for people who want to apply Machine Learning without worrying too much about the theoretical aspects of it.

автор: 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.

автор: Roger A G

Jun 03, 2019

Excellent course! It teaches you the basics of Machine Leaning, and merges the knowledge already acquired in the first module

автор: Stephen

May 03, 2019

Had all the basics of Machine Learning algorithms, but they need to update the syllabus with some trending boosting concepts

автор: Ivan Y

Oct 24, 2018

Great! loved the final project, which is a machine learning project that you can actually put on your resume and talk about!

автор: Muhammad S

Apr 01, 2020

I am very satisfied with this course. I learnt a lot of techniques from the course that I can apply in my research project.

автор: Hrishikesh B

Mar 14, 2019

very good course for intermediate level learners .learned a lot in such a short time.thanks to prof.Kevyn Collins-Thompson.

автор: Bui T D

Oct 30, 2018

It is a great course with best practices. Thank you for your time and consideration. I learnt many things from your course.

автор: 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.

автор: Boyan Z

Dec 16, 2019

A very useful course that gives very good overview for the applied side of machine learning for solving various problems.