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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4.6
4,659 個評分
804 條評論

課程概述

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....

熱門審閱

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 - Applied Machine Learning in Python 的 200 個評論(共 787 個)

創建者 Vishy R

Feb 25, 2020

Amazing course. Initially frustrating, as assignments are pretty hard, but once you get into it, the learning is just immense. Thank you, Professors!!

創建者 Rafael A A S

Jul 30, 2018

Extraordinary course. Contents were very useful for the learning process. This is a good start for diving into this amazing world of machine learning.

創建者 Aneek A

Sep 30, 2017

Very informative and covers Machine Learning (along with scikit learn) in great breadth!! Would love to see a bit more challenging assignments though.

創建者 Alexandre G

Oct 24, 2019

This is a very good course. Probably, much time should be given, especially for Week 2 and Assignment of Week 4. Thank you very much for the course!

創建者 Surya P M

Apr 01, 2019

complex topics are explained in a simple way. coding assignments, quiz helped a lot to learn and apply numerous machine learning concepts perfectly.

創建者 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.

創建者 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!

創建者 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!

創建者 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.

創建者 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.

創建者 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

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 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!

創建者 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

創建者 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!

創建者 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!

創建者 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.

創建者 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.

創建者 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! :)

創建者 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.

創建者 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.

創建者 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.