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

4.6
stars
8,453 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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1451 - 1475 of 1,539 Reviews for Applied Machine Learning in Python

By Deleted A

•

Mar 27, 2018

The videos are way too long and very monotonous. They should be cut down and reduced. The maximum length they should be is 5-6 mins other wise they becoming distracting.

The course content is good though. Quite easy to understand but going through the videos is a chore.

By Deleted A

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Sep 12, 2020

I think that the subjects are very advanced. There should be a more clear specifications of prerequisites for the course. I had to look for lot of help outside the materials provided for doing the activities. The course is fine if you have the apropiate skils though.

By vikram m

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Aug 26, 2019

It's a good course, but a quick one. One needs to have a beforehand knowledge of all the algorithms as they are not discussed in details. State of the art is not mentioned. Implementation and best practices are present, along with pros and cons of each algorithm

By Claire Z

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Jul 20, 2019

The course is quite high-level. There is nothing wrong with an applied course being high-level. The material is easy to follow, the quiz is a bit challenging but the homework assignments are quite easy to pass. I prefer a course with more fundamental details.

By Raymond C

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Jan 27, 2019

The course is too tight, just 4 weeks cannot master the machine learning. This course can splitted into 2, in order to capture more on the deep learning and unsupervised learning, which are important, but being categorized as option in the course.

By Suhas A B

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Dec 31, 2020

Good content but too fast paced for someone without even the slightest basics on ML. The first 2 courses in the specialization did not prepare for this course. To make full use of the course get ML basics right and then maybe come here

By Hilal I

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Jun 29, 2023

Full with a lot of important information, however the delivery is very weak. With all respect, this was one of the most boring courses I have ever had. Seeing the unmotivated instructor makes the course very difficult to follow.

By Tracy S

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Jul 31, 2017

the second assignment was a little beyond what was taught in the lecture. others are fine.

big suggestion: please please have a better auto-grader. Most of my time was spending on how to battle the auto-grader instead of coding...

By Nicolas B C G d B S S - N 8

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Jan 20, 2024

The course is basic, and they skip too many details; often, it seems like they only created the course for the sake of it. I think they try to cover too many topics in a single course. The Python codes are also quite average.

By Rahma S E

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Nov 26, 2021

there is a little suggestion from me, the correction system is a bit strange when I did the test when I ran it it worked, but after submitting 0/100. But I kept trying to submit, suddenly it corrected itself and had a value.

By Berk A

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Nov 17, 2022

What is taught in the lessons and what is asked in the assignments have nothing to do with it. Assignments are too difficult. You can try if you want to challenge, but if you're just learning, I don't recommend it.

By Sukesh K

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Jun 14, 2020

Course is well structured, course material also is well defined and learning is excellent. Though Instructor's communication is very laidback. Should have more engagement in tone and connect with enthusiasm.

By Jan

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Aug 7, 2017

Quick tutorial-like overview. Autograder is not too verbose and as a result I spent some time struggling with debugging the code rather than figuring out how to solve machine learning related problems.

By Fernanda T

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Aug 4, 2020

Good content and I learned a lot. However, the instructor made too many mistakes during the lectures and the assignments also have mistakes that need to be fixed by the students.

By Ketan L

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Jun 4, 2018

Follow the course with introduction to ML with python to have descent understanding. Instructor won't be able to keep one interested for long. Exercises could have been tougher.

By Victor E

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Aug 16, 2017

Two point: 1) you can learn a lot here, 2) imagine you are shown a hammer but never explained how to hit a nail. Two previous courses in the specialization do both.

By Kareem H

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Mar 3, 2020

Course instrutor and materials are needed to be improved as they are very poor

Assigments\Quizes are very good and they are the mainly root cause for this rating

By Thomas B

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Jul 7, 2018

Some very good practical advice like dummy testing or data leakage issues Some trivialities and repetitions. Python code could have been a bit better commented

By BIRENDRA H S

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Jun 13, 2020

there should be some low level usage of sentences for a intermediate programmers,most of times it bounces up the mind ,not able to get the required concept

By Baizhu

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Jul 5, 2017

Know some existing machine learning functions and packages from sklearn, but really don't know how to improve prediction accuracy within each function.

By Maguys C

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Feb 10, 2022

There are many errors in the videos. There are not enough real-life examples on how to apply the models. The assignment estimates are realistic.

By Matteo B

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Aug 10, 2019

Assignments are not really supported by the material provided (videos). The level is not balanced. Some bugs in the assignment code as well

By Berkay A

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Jul 15, 2020

This course seems hard and actually I did not like the syllabus so much. Assignments were so hard and there were some issues in Notebooks.