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

4.6
stars
7,241 ratings
1,318 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

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!!

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.

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951 - 975 of 1,295 Reviews for Applied Machine Learning in Python

By xuhp

Nov 13, 2018

great

By shubham

Mar 17, 2018

great

By MD M H

Nov 4, 2020

Nice

By ISHAN M

Oct 30, 2020

Good

By �SADHARAN G

Aug 10, 2020

good

By RAGHUVEER S D

Jul 25, 2020

good

By N. S

Jul 7, 2020

good

By Arif S

Jul 1, 2020

good

By parmar p

May 18, 2020

nice

By Miriam R

Dec 26, 2019

good

By Light0617

May 12, 2019

nice

By Shishir N

Jan 9, 2019

N

i

c

e

By Jimut B P

Oct 8, 2018

Nice

By Yi-Yang L

Jul 3, 2017

Nice

By SURAJ K

Jun 23, 2020

osm

By Shilpi G

Jun 2, 2019

...

By Magdiel B d N A

May 10, 2019

ok

By PREDEEP K

Nov 24, 2018

ok

By Andrew G

May 16, 2019

T

By Junaid L S

May 14, 2019

G

By Thomas

Mar 6, 2018

g

By Oleh Z

Feb 27, 2018

G

By Piotr B

Jun 1, 2017

a

By Martín J M

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 Jun-Hoe L

Jun 3, 2020

My actual rating is 3.5 stars. This is the best course yet in this Specialization.

Pros: I prefer Professor Collin-Thompson's delivery compared to Professor Brook in the previous modules. I think he gives a good overview and sufficient depth for an applied course, compared to Professor Brooks which I find to be quite superficial most of the time, and weirdly detailed in other parts. Assignment is good enough for reinforcement learning and definitely better planned. I also appreciate the link to additional readings which are quite informative.

Cons: Assignment auto-grader. This is still the biggest letdown of all the courses in this specialization Codes which work on your laptop or suggested elsewhere on Stackoverflow etc fails to pass the autograder, so 30-40% of the time of the assignment is spent on wrangling the code to pass the autograder.

Note: If i haven't taken a Machine Learning course by Professor Andrew Ng, this course would definitely be much harder. This course doesn't go to much into the background knowledge,and they mentioned this many times. But I appreciated the applied aspect, since this was what I was looking for.