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Bewertung und Feedback des Lernenden für Applied Machine Learning in Python von University of Michigan

4.6
Sterne
7,973 Bewertungen
1,452 Bewertungen

Über den Kurs

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-Bewertungen

AS

26. Nov. 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.

OA

8. Sep. 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

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351 - 375 von 1,442 Bewertungen für Applied Machine Learning in Python

von Shuyi Y

27. Juni 2017

This course is great because I received so much training in applying the ML packages and functions python. A lot of hands-on experience!

von Marcelo P

9. Juli 2019

Great course! Superb professor! Very well organized and structured. Lots of useful optional articles and videos. Learned a lot. Thanks!

von Nguyen K T

25. Juni 2019

A very practical course and it helps me to understand more about machine learning theory. After all, this is a great course. Thank you.

von Mehmet F C

27. Dez. 2018

good one to quickly start learning ML - covering models, what they do, and how to tune them. Not going deep into the "how" models work.

von Shao Y ( H

8. Sep. 2017

Very good survey of all fundamental topics of machine learning! Good resources for preparation for technical data science interview! :)

von INHOI J

25. Apr. 2020

Great course. Professor delivered very complicated concepts of machine learning very easily. Quiz and assignments were very helpful.

von Keith M

12. Okt. 2020

Excellent course. Very detailed, very interesting, a lot to get through in each week. Lots of great examples of code and scenarios.

von Quan S

8. Mai 2019

Course materials are very systematic and instructive, and the professor teaches very clearly. I like this course and recommend it.

von Flavia A

11. März 2018

Practical class to learn well-known models and scikit-learn. The practice tests are great to help you move from theory to practice.

von Aniket K

1. Juli 2020

Good Course. Not for beginners starting with Machine Learning. Intermediate level. Prior knowledge of python libraries would help.

von Émile J

19. Mai 2020

The exercices and evaluations are more complex than in the previous courses in this short program, but also much more instructive.

von Himanshu B

15. Mai 2020

It was really an excellent well designed course, I gained valuable information that I will use as a business analytics in future.

von Ivan S F

23. März 2019

Very good course. Not very deep, but definitively very wide and appropriate for an overview course of machine learning in python.

von abdulkader h

4. Juli 2017

I appreciate so much this course even it was so dense and slitly short. It would be useful to extend it over several weeks again.

von Js S

13. Apr. 2022

F​inal assigment was very challenging but necessary to effectivly learn how to apply the ML technics provided during the course.

von usama i

12. Okt. 2020

Excellent course to understand and learn about how to work with available classifiers in scikit learn. Thanks for this course :)

von Ari W R

28. Aug. 2020

it is a pleasure to learn about machine learning course. I can remind and study again about the main things in machine learning.

von Jason L

26. Aug. 2020

Very solid course. Covers so many key machine learning concepts in a short period of time. Week 2 is intense - but awesome!

von Mahindra S R

27. März 2020

Useful for understanding the application part of ML whereas Andrew Ng's course gives a more in-depth understanding of the topics

von SURENDRA O

25. Dez. 2018

The course was very well designed. The pace of the lectures are perfect unlike other course when the instructor moves very fast.

von Yiwu T

16. Apr. 2021

Broad coverage.

Good project assignment.

Staff not answering questions very promptly at discussion forum.

Cannot download slides.

von Ram N T

2. Jan. 2020

The course material and Professor Kevyn Collins-Thompson is awesome. A person who's seeking to learn ML should try this course.

von STEVEN V D

21. Jan. 2018

World class course.

Covers a lot of core machine learning subjects in an accessible way with a practical focus in Python.

Thanks!

von Peter D

6. Nov. 2017

Nice pragmatic approach how to apply machine learning. Compelling examples, datasets and useful tips how to visualise features.