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

4.6
stars
8,529 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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1401 - 1425 of 1,553 Reviews for Applied Machine Learning in Python

By Burak

•

Sep 30, 2018

good for scikitlearn.

By David P

•

Aug 4, 2021

A really good course

By Bama

•

Jul 11, 2020

This course is good.

By Abhav T

•

Jun 3, 2020

Nice course to study

By Boris D

•

Jan 17, 2021

Quite challenging.

By Shashi K

•

May 18, 2020

very good learning

By HAMZAOUI M

•

Jul 25, 2019

HARD BUT GOOD

By Dr. K

•

Oct 2, 2020

nice course

By Aditya V

•

Jul 3, 2018

Excellent!!

By Ishan S

•

Jul 23, 2017

Awesome !!!

By KILLANI T

•

Jun 10, 2020

hard a bit

By Diego F M A

•

Jun 29, 2022

Excellent

By Deepak T

•

Jan 13, 2020

Very Good

By Md J A

•

Aug 18, 2017

very good

By MOHD A

•

Sep 10, 2020

perfect

By NITYA B 2

•

Oct 17, 2021

Good

By tanmoy p

•

Dec 18, 2020

good

By Learner

•

Nov 28, 2020

Good

By Anant k

•

Sep 26, 2020

GOOD

By Sajal P

•

Aug 12, 2020

....

By Latha B N

•

Jul 9, 2020

Good

By Yzeed A

•

Oct 30, 2019

Good

By Manas C

•

Dec 12, 2021

ok

By Ketan S R

•

Jul 4, 2019

.

By Shubham J

•

Mar 2, 2022

Here's my review for this course - The good aspects - - This course served as a good refresher for traditional ML concepts like Regression, Classification, and Model Evaluation, along with hands-on exercises in Python. - Assignments need effort, have good exercises & force you to think. You cannot simply watch the lectures & complete them straight away. - I especially liked the module about Data Leakages and how it impacts our model's performance. Scope for Improvement - - Some concepts like Classification models are explained pretty well whereas others such as Regression, and Unsupervised learning (Clustering, Anomaly Detection) are quite rushed. - There are some obvious errors in the assignments and auto-grader, missing files, some clearly vague questions. The discussion forum is riddled with similar questions for these errors - they could have fixed it years ago but chose not to. - Not much depth in the topics - beginners will have difficulty understanding pitfalls of certain models, how real-world data mining works, and how to select features and models.

If you're a beginner - it will give you a good overview of traditional ML models and implementation in Python. Good to try, but you need to spend a lot of time for self-learning the concepts, specially the mathematics behind these algorithms.