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Learner Reviews & Feedback for Applied Machine Learning in Python by University of Michigan

4.6
stars
8,587 ratings

About the Course

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....

Top reviews

BS

Sep 17, 2020

Great content and good instruction. Need to fix the files in the assignments though. It's hard to keep track in the forums and frustrating go back and forth to find out why it's not working.

MB

Jun 18, 2017

Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.

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1551 - 1562 of 1,562 Reviews for Applied Machine Learning in Python

By Topiltzin H

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Mar 22, 2021

Course was not as expected, I think XG Boost for instance is quite large and was covered in less than 20 minutes.

By SAMADRITO B

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Mar 19, 2021

The course is full of faulty assignment grader and the concepts given are not up to the mark

By Aditya M

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Jul 17, 2020

Can't the lecturer use proper slides with proper diagrams for a better explanation.

By Deyner E L P

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May 29, 2022

Demasiados errores a la hora de enviar los laboratorios.

By SHREYAS D

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Aug 14, 2020

Things in the beginning are not explained properly

By Joe R

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Mar 31, 2021

Terrible lectures - assignments were good though

By Hamady G

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Feb 20, 2023

je souhaite me desincrire a cette formation

By Varun D

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Jul 19, 2022

A lot of the course has much to improve.

By Konark Y

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May 10, 2020

many issues while submitting assignments

By Oleg G

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May 16, 2020

enrolled by mistake want to u nenroll

By Christos B

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Jan 3, 2025

Very poor grading system

By SHREYANSH A 2

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Mar 1, 2023

lund course