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Applied Machine Learning in Python に戻る

Applied Machine Learning in Python, ミシガン大学(University of Michigan)

4.7
3,369件の評価
607件のレビュー

このコースについて

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....

人気のレビュー

by 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!!

by 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件のレビュー

by 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.

by Stanley Cheng

May 21, 2019

Excellent. Learned a lot!

by 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

by Hanchi Wang

May 18, 2019

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

by jose H Chiriboga

May 17, 2019

Comprehensive & thorough

by Xia liu

May 16, 2019

GREAT

by Andrew Ghattas

May 16, 2019

T

by Junaid Latif Shaikh

May 14, 2019

G

by Edgar Miguel del Jesús Guzmán Blanco

May 13, 2019

Excelente

by Light0617

May 13, 2019

nice