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学生对 密歇根大学 提供的 Applied Machine Learning in Python 的评价和反馈

4.6
6,846 个评分
1,238 条评论

课程概述

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....

热门审阅

FL
Oct 13, 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 8, 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

筛选依据:

376 - Applied Machine Learning in Python 的 400 个评论(共 1,218 个)

创建者 Ahmad A

Jul 11, 2020

Excellent Course each topic is both theoretically as well as as practically explained. Really a good course

创建者 Akshay S T

May 24, 2020

Very Intuitive and helpful course for clearing concepts of machine learning and Python's SciKit Learn module

创建者 Nitin K

Apr 22, 2019

Great Course. Helped me to learn the concepts of Machine Learning and uses of respective Sklearn libraries.

创建者 Mohamed A M A

Jan 19, 2019

The theoretical part is comprehensive with an excellent balance between the theory and practical exercises.

创建者 HISHAM I A

Nov 5, 2018

Excellent collection of various types of Machine Learning Algorithms with visual demonstration and example.

创建者 Rahul S

Dec 8, 2019

This course is Beautifully crafted to cover most of the important concepts of supervised machine learning.

创建者 Christian E

Jan 19, 2019

Content and phase are very good. Very clear explanation of topic by the instructor. Appreciate it so much.

创建者 Anurag W

Jul 18, 2019

This Course really provides great learning on Advance Machine learning techniques with Python application

创建者 Matt E

Aug 29, 2017

Learned a lot in this course! Much better than the previous two and also taught by a different professor.

创建者 Alexander A

Aug 16, 2020

Excellent Course. The only one problem is the duration of videos. The codes in Jupyter are very elegants

创建者 Miguel Á B P

Jul 28, 2018

What a challenge. Incredible course, no words. Excellent pedagogy from professor Kevyn Collins-Thompson.

创建者 Alejandro R

Jul 8, 2018

Good choice for Machine Learning introduction, Data Analysis in Python and applied statistical concepts.

创建者 Mile D

Oct 17, 2017

After this course you will be able to do your own analysis using machine learning which is really great.

创建者 Shashwenth.M

Dec 19, 2019

Seriously THE BEST for gaining a broad knowledge about machine learning techniques in a applied manner.

创建者 Min L

Feb 6, 2019

A very good course to start journey on data science. Good combination of reading, lecture and practice.

创建者 Mikhail E

Sep 22, 2020

Great course, though was a bit difficult for me, as I wasn't very familiar with math side of the issue

创建者 Francesco S

Mar 20, 2018

Excellent couse, I've gained real knowledge and the lecture is very thorough! Challenging and intense.

创建者 Oumeyma F R

Dec 23, 2019

What I loved about this course is the consistency of its content and the quality of its presentation.

创建者 Zachary Q

Aug 19, 2019

Was a great class where I learned to apply existing knowledge about ML to the actual background info!

创建者 Muhammad A R

Sep 24, 2018

Covers most of the basic supervised Machine learning Algorithms in SciKit-Learn from application POV.

创建者 KylinMountain

Jun 7, 2018

It's very impressive.

I suggest If we add a kaggle competition as a overall summery, that'll be great.

创建者 Megan J

Dec 31, 2018

In depth understanding is required to complete the assignments. Challenging without being demanding.

创建者 Evan G

Jul 23, 2018

Quick way to get exposed to supervised learning algorithms. Lays a nice foundation for ML in python.

创建者 David R

Jul 21, 2020

Nice survey of machine learning techniques and tutorial on the scikit-learn toolbox. Very helpful.

创建者 Yashar Z

Aug 29, 2017

This course is one of the best courses for ML. The teacher teaches concepts clearly and completely.