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Opiniones y comentarios de aprendices correspondientes a Applied Machine Learning in Python por parte de Universidad de Míchigan

4.6
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7,522 calificaciones
1,372 reseña

Acerca del Curso

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....

Principales reseñas

AS
26 de nov. de 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
13 de oct. de 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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351 - 375 de 1,356 revisiones para Applied Machine Learning in Python

por Ivan S F

23 de mar. de 2019

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

por abdulkader h

4 de jul. de 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.

por usama i

12 de oct. de 2020

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

por Ari W R

28 de ago. de 2020

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

por Jason L

26 de ago. de 2020

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

por Mahindra S R

27 de mar. de 2020

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

por SURENDRA O

25 de dic. de 2018

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

por Yiwu T

16 de abr. de 2021

Broad coverage.

Good project assignment.

Staff not answering questions very promptly at discussion forum.

Cannot download slides.

por Ram N T

2 de ene. de 2020

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

por STEVEN V D

21 de ene. de 2018

World class course.

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

Thanks!

por Peter D

6 de nov. de 2017

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

por Manoj K K M

30 de jun. de 2018

For applied machine learning, outstanding. It could be improved with bit more theory, which gives more insight to the concept.

por Shrish T

20 de ago. de 2017

Very good course, for people who want to apply Machine Learning without worrying too much about the theoretical aspects of it.

por Raga

9 de jun. de 2017

Very well designed courses! There are many materials to go in depth even if you have done Python Machine Learning in the past.

por Roger A G

3 de jun. de 2019

Excellent course! It teaches you the basics of Machine Leaning, and merges the knowledge already acquired in the first module

por Stephen S

3 de may. de 2019

Had all the basics of Machine Learning algorithms, but they need to update the syllabus with some trending boosting concepts

por Ivan Y

24 de oct. de 2018

Great! loved the final project, which is a machine learning project that you can actually put on your resume and talk about!

por Muhammad S

1 de abr. de 2020

I am very satisfied with this course. I learnt a lot of techniques from the course that I can apply in my research project.

por Hrishikesh B

14 de mar. de 2019

very good course for intermediate level learners .learned a lot in such a short time.thanks to prof.Kevyn Collins-Thompson.

por Duy B

29 de oct. de 2018

It is a great course with best practices. Thank you for your time and consideration. I learnt many things from your course.

por Mkhitar T

13 de ago. de 2020

If anyone wants to gain practical skills in Machine Learning with Python, this course for them. Thank you for this course.

por Martin U

11 de ene. de 2019

Tough class, learned not to give up and keep trying. Even went back and redid some quizzes in order to get a better grade.

por Boyan Z

16 de dic. de 2019

A very useful course that gives very good overview for the applied side of machine learning for solving various problems.

por TEJASWI S

1 de ago. de 2019

Concepts were clearly taught and helped me gain knowledge in techniques used in machine learning. Recommend it to others.

por SIMRAN S

6 de sep. de 2020

An apt course who want to become Data scientist beautiful Basics of Machine Learning which is one of major topics in it.