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

4.6
stars
Оценки: 4,541
Рецензии: 784

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

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

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176–200 из 766 отзывов о курсе Applied Machine Learning in Python

автор: Jay N

Oct 18, 2018

very very excellent, got to learn whole lot of machine learning models and approaches. i'm straight away going for kaggle competitions after this.

автор: Carlos F P

Sep 20, 2018

It gives a great overview of different machine learning methods. I found useful information that can be missing in other ML courses. Great course!

автор: 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!

автор: 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.

автор: 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.

автор: 李子杰

Aug 30, 2018

Easy for beginner to follow. After finishing the course,I'm able to apply simple machine learning algorithms to area I'm currently working on

автор: Santhana C

Aug 05, 2017

Nice Course! Lots of useful information packed in 4 weeks. Be prepared spend some extra time if you want to really benefit from this course.

автор: 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.

автор: Juan R C C

Oct 25, 2017

Good course, content and teaching. Very good weekly assignments allow students to well consolidate course contents on real world practices.

автор: Nattapon S

Aug 03, 2017

It is a good class. I learn a lot from this course. It is a concise starting course for Python machine learning. I recommended this course.

автор: Sunny K L W

Sep 08, 2018

Great Course with high practicality. Need more lectures on how to process categorical data. Read the Forum if you encounter any question!

автор: Fengping W

Mar 28, 2018

It is really a good one, and I learn a lot here, both for theory and applied skills. And the reading materials are really good resources

автор: Shuyi Y

Jun 28, 2017

This course is great because I received so much training in applying the ML packages and functions python. A lot of hands-on experience!

автор: Marcelo d S P

Jul 09, 2019

Great course! Superb professor! Very well organized and structured. Lots of useful optional articles and videos. Learned a lot. Thanks!

автор: Nguyen K T

Jun 25, 2019

A very practical course and it helps me to understand more about machine learning theory. After all, this is a great course. Thank you.

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: 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: 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: 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.