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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
7,981 個評分
1,452 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

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!!

OA

2017年9月8日

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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426 - Applied Machine Learning in Python 的 450 個評論(共 1,442 個)

創建者 Thomas S

2021年6月21日

A very good review of important fundamental concepts in Machine Learning focusing on the usage of Sklearn.

創建者 Rahul S

2019年12月8日

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

創建者 Chris E

2019年1月19日

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

創建者 Lari L

2021年7月3日

The course gives deep knowledge on the subject as well as best practices and strong practice assignments.

創建者 abdelrahman a

2020年12月9日

the most interesting thing in the course was treating the students as if they are already data scientists

創建者 Anurag W

2019年7月18日

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

創建者 Matt E

2017年8月29日

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

創建者 Fettah K

2021年5月9日

Taking these lessons from some of the world's most prestigious universities and professors is priceless.

創建者 Alexander A

2020年8月16日

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

創建者 Miguel Á B P

2018年7月28日

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

創建者 Alejandro R

2018年7月8日

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

創建者 Mile D

2017年10月17日

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

創建者 Allyson D d L

2021年12月3日

Another good course of the specialization. The videos are a little boring but the assignments are good.

創建者 Shashwenth.M

2019年12月19日

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

創建者 Min L

2019年2月6日

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

創建者 Mikhail E

2020年9月22日

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

創建者 Francesco S

2018年3月20日

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

創建者 OUMEYMA F

2019年12月23日

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

創建者 Zachary Q

2019年8月19日

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

創建者 Muhammad A R

2018年9月24日

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

創建者 KylinMountain

2018年6月7日

It's very impressive.

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

創建者 Megan J

2018年12月31日

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

創建者 Evan G

2018年7月23日

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

創建者 ETENDRA V

2021年9月30日

Very Good Course. I'll highly recommended pre requisites are basic knowledge of python programming.

創建者 David R

2020年7月21日

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