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

4.6
stars
7,151 ratings
1,298 reviews

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

AS
Nov 26, 2020

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

FL
Oct 13, 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!!

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1251 - 1275 of 1,277 Reviews for Applied Machine Learning in Python

By Chirag S

May 24, 2020

The content was less informative and audio quality was poor. However, assignments are fun completing.

By Rohit S

May 21, 2020

The online grader needs to be updated as there is constant error showing up though our code is right

By Gilad A

Jun 27, 2017

The last assignment was super. apart for it, the assignments and the course were too easy

By Sai P

Jun 3, 2020

There were a few corrections made during the videos which ended being quite confusing.

By Philip L

Oct 31, 2017

The assignments are extremely difficult, professor is a bit dry during lectures.

By Pakin S

Jan 10, 2020

How can i pass without reading discuss about problem with notebook

By Hao W

Aug 27, 2017

The homework is too easy to improve our understanding of ML

By M S V V

Jun 29, 2020

Too much of information compressed within a short span.

By José D A M

Jun 21, 2020

Too fast, yet too difficult. Needs deeper explanation.

By Navoneel C

Nov 21, 2017

Nice and Informative but not practically effective

By Priyanka v

May 8, 2020

if it is more detailedthen it will be more useful

By Sameed K

Mar 15, 2018

have to figure out a lot of things on you own.

By Andy S

Jun 4, 2019

It could have been better with more examples.

By Shan J

Apr 12, 2020

The explanation could have been much better.

By Jeremy D

Jul 10, 2017

The topics were good, but too many were d

By Ryan S

Dec 12, 2017

Homeworks are inconvenient to submit

By PIYUSH A

May 16, 2020

The narration was a bit boring.

By shreyas

Jun 29, 2020

Teacher wasn't very good

By Abir H R

Jun 30, 2020

very long videos

By Wojciech G

Oct 28, 2017

To fast paced.

By Aarya P

Sep 30, 2020

Really disappointed with the course ...you may ask why??

The first thing is the instructor , super boring. The instructor (with all due respect) was very dry and the lectures were super uninteresting. When he keeps on talking code, but doesn't really explain stuff. The material and lectures were dry and colorless.

Me without having good statistics background had huge difficulties understanding the concepts. Please i recommend everyone to have good knowledge in statistics before starting the course. ABSOLUTELY NOT THE BEGINNER LEVEL AND NEITHER INTERMIDIATE LEVEL .the course is quiteeeee difficult.

You also need to have a lot of self study , which i am not a big fan of. I hope they make the course more fun rather than a man constantly talking on the screen .

By Oswaldo C

Aug 22, 2020

Los videos no son suficientemente extensos ni para explicar el código, ni para explicar la teoría detrás de los algoritmos, se queda a medio camino de los dos siendo insuficiente en ambos casos

By David C

Nov 8, 2020

Not as good as prev. courses. Univ. of mic. should update or get ride of this module

By Aditya M

Jul 17, 2020

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

By SHREYAS D

Aug 14, 2020

Things in the beginning are not explained properly