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Applied Machine Learning in Python, University of Michigan

(3,362 ratings)

Über diesen Kurs

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....


von 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!!

von 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

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588 Bewertungen

von Krishna

May 22, 2019

Course content is very nice and covered aptly. I feel that some where more depth was necessary to understand the algorithms.

von Stanley Cheng

May 21, 2019

Excellent. Learned a lot!

von Stephan K.

May 19, 2019

excellent, practical introduction to (mainly) supervised machine learning in scikit learn. Next to Python specific handling of models, also conceptual issues like parameter tuning, feature pre-processing and - very nicely - data leakage are explained. examples can get tricky without solid grasp of numpy and pandas packages

von Hanchi Wang

May 18, 2019

Good content, some coding assignments are hard to submit(csv file not found)

von jose H Chiriboga

May 17, 2019

Comprehensive & thorough

von Xia liu

May 16, 2019


von Andrew Ghattas

May 16, 2019


von Junaid Latif Shaikh

May 14, 2019


von Edgar Miguel del Jesús Guzmán Blanco

May 13, 2019


von Light0617

May 13, 2019