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Applied Machine Learning in Python, 密歇根大学

4.6
2,916 个评分
542 个审阅

课程信息

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

创建者 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!!

创建者 SS

Aug 19, 2017

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action

筛选依据:

525 个审阅

创建者 Michael Tondu

Feb 21, 2019

Great content and reference materials

创建者 James Sheldon

Feb 21, 2019

Very excellent course. Well done explanations even if there is some language confusion. Taking the time to really understand the concepts makes all the difference.

创建者 Purna Chander Kathula

Feb 21, 2019

It's a superb course well organised with good and real time examples.

创建者 Darius Tamašauskas

Feb 19, 2019

Great course for learning how to apply Machine Learning algorithms with Python.

创建者 Sumit Mishra

Feb 19, 2019

This is a very good course about How to apply Machine Learning but I think before taking this course the student should take the Andrew Ng machine learning course by Stanford University to Learn the Important Mathematics behind the ML algorithms

But Enjoyed this course a lot

thank you

创建者 Kevin

Feb 15, 2019

It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken. Strongly recommend for your knowledge and career advance. Great professor!

创建者 Anne Estoppey

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

创建者 Pieter Joan Van Voorst Vader

Feb 13, 2019

Inspirational course, learning you in a comprehensive manner, a thorough approach to machine learning with the target specific peculiarities and possible pitfalls.

创建者 Shaukat

Feb 11, 2019

excellent course

创建者 CMC

Feb 09, 2019

A little dated. Overall a good introduction. The informal explanation of SVM was particularly effective.