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

4.6
stars
8,515 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

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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1126 - 1150 of 1,550 Reviews for Applied Machine Learning in Python

By RAGHUVEER S D

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Jul 25, 2020

good

By N. S

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Jul 7, 2020

good

By Arif S

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Jul 1, 2020

good

By parmar p

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May 18, 2020

nice

By Miriam R

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Dec 26, 2019

good

By Light0617

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May 12, 2019

nice

By Shishir N

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Jan 9, 2019

N

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By Jimut B P

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Oct 8, 2018

Nice

By Yi-Yang L

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Jul 3, 2017

Nice

By SURAJ K

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Jun 23, 2020

osm

By Shilpi G

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Jun 2, 2019

...

By DURGE A J

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Aug 28, 2024

ok

By Magdiel A

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May 10, 2019

ok

By PREDEEP K

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Nov 24, 2018

ok

By Jintao M

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Feb 1, 2023

。

By Souvik G

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Aug 23, 2021

5

By Deelaka S

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Jun 16, 2021

s

By Andrew G

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May 16, 2019

T

By Junaid L S

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May 14, 2019

G

By Thomas

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Mar 6, 2018

g

By Oleh Z

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Feb 27, 2018

G

By Piotr B

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Jun 1, 2017

a

By Martín J M

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Sep 20, 2020

Course is excellent in content. Not heavy in mathematics (altough, I would recommend reading how models are supposed to work), the objectiv eis to have a practical understanding of how machine learning is applied and the important concepts to consider for a succesful model building. The focus is to have hand-on experience with the sklearn library.

I don't grant 5 starts (I hesitated for 4), as the course was designed back in 2018, therefore, you sometimes struggle with legacy libraries. Another issue, is that there are some hiccups when it comes to assignment uploads (for instance, the address of csv files!). As a student, this will make you hesistate and question wether the instructor screwed up with the autograder or not, which IS stressful.

Quiz 4 suddenly became non-forgiving, multiple choice answer have to be answered with 100% certainity to score full point. Quite anti-climatic, considering that previous quizes didn't work like that.

Final assignment is quite challenging, and might make the new student suffer.

I appreciate the instructors and Kevyn Collins for this great course. Now that I have a better picture, I get insights on how to focus my research efforts in sensor research and development.

By Carolyn O

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Jan 19, 2020

I had no ML background, although I have the math the models are based on. The material seemed more than week's worth for a couple of weeks. The quizzes make sure you don't miss the key points you need to take away and need for the assignment. Most information or key words are in the slides, but course expects you to be independent enough (intermediate) to learn closely related ideas on your own via StackOverFlow and discussion forums. The discussion forums were especially helpful for this course, but then online discussions makes it more studying alone. Discussions helped me trouble-shoot and get better ideas how to approach the problems generally. I can explore and use ML and sklearn on my own, which thankfully seems to be a goal of this professor. No material could be left out, but when more videos, better longer time estimate for the week would be nice.

By Shah M

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Jul 24, 2023

Solid course with valuable content, but it's worth considering reading the source material, Géron's Hands-on Machine Learning, for a deeper understanding of the topics. While the lectures cover the material, Géron's book delves into the concepts more comprehensively.

One drawback is the presence of errors in the last three assignments, making it challenging to achieve full marks. It would be beneficial for the course creators to address these issues promptly. Additionally, the course's reliance on a black box approach might not be to everyone's liking. I highly recommend supplementing the learning by exploring the underlying math behind each algorithm and validation method discussed throughout the course.

Overall, the course content is great! But the above reasons prevent me from giving it a five-star review