Chevron Left
返回到 Applied Machine Learning in Python

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

4.6
7,060 個評分
1,287 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

AS
2020年11月26日

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

FL
2017年10月13日

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!!

篩選依據:

301 - Applied Machine Learning in Python 的 325 個評論(共 1,265 個)

創建者 Carlos F P

2018年9月20日

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

2017年7月9日

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 Jack R

2020年9月23日

Great course. A LOT of information but great job at teaching conepts and how to apply them. It got me really interested in Deep Learning and MLP.

創建者 Lucas G

2017年6月5日

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

2019年2月8日

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 Bruno S F C H S

2020年7月5日

Excellent course to do an overview of many ML algorithms, and with good assignments that help me to fix all the subjects that I have learned!

創建者 Ari S P

2020年5月11日

From several MOOCs that focus on ML. I love this course to understand the fundamental off ML and I can easily apply this course in my project

創建者 李子杰

2018年8月30日

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 Aino J

2020年6月21日

Practical, applied, and a good overview of how to apply different (mainly supervised) machine learning algorithms using python scikit-learn.

創建者 Santhana C

2017年8月5日

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 Eddie G

2021年1月18日

This course has the perfect combination of theory and practice. It's Intense for a beginner In machine learning but Is absolutely worth It.

創建者 Rajendra S

2019年1月11日

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

2017年10月25日

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

2017年8月3日

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 JOSE A P A

2020年7月14日

Un excelente curso para reforzar lo aprendido en el curso Minería de Datos para la Toma de Decisiones que se dicta en la Universidad Esan.

創建者 Ramon S

2020年12月7日

Excellent! I had previously done a course on machine learning and it left me with big holes in my knowledge, this really clear things up!

創建者 Moustafa A S

2020年7月28日

GREAT COURSE!, this is one of the greatest courses for applying machine learning and data science algorithms and skills, great great job.

創建者 Ritesh P N

2020年7月19日

It was amazing course for applied machine learning. The tutor was good teaching core concepts of machine learning algorithms step by step

創建者 Chak W

2018年9月8日

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

2018年3月28日

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

2017年6月27日

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 P

2019年7月9日

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

2019年6月25日

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

2018年12月27日

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

2017年9月8日

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