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미시건 대학교의 Applied Machine Learning in Python 학습자 리뷰 및 피드백

4.6
별점
7,863개의 평가
1,430개의 리뷰

강좌 소개

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....

최상위 리뷰

FL
2017년 10월 13일

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
2020년 11월 26일

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.

필터링 기준:

Applied Machine Learning in Python의 1,416개 리뷰 중 226~250

교육 기관: Prateek D

2021년 10월 19일

Best course that I did on coursera. Learnt a great deal helped me a lot, thanks a lot University of Michigan and coursera as this course taught so many things which helped me to get placed in college placements.

교육 기관: Piotr K

2017년 11월 29일

Great course to gain basic ML skills and start building first models. Excellent starting point. Combined with Andrew Ng`s course on Machine Learning it`s great foundation for futher development as AI specialist.

교육 기관: Edwin V

2020년 6월 17일

Machine Learning Fundamentals are taught in concise and easy to understand manner. Some of the ML algorithms such as Kernelized SVM have been explained brilliantly. Thanks for putting up this wonderful course.

교육 기관: Limber

2017년 12월 3일

It is a very practical course if you have learned the Andrew Ng's Machine Learning course. It is much much more practical and I have gained a lot from it. I really wish I could learn it soon. Thanks very much.

교육 기관: Ayush D

2020년 5월 30일

Learned a lot from this course, very informative. One thing have to say that its not for absolute beginners, this course required prior knowledge of ml and python which will ease completion of course. Thanks!

교육 기관: Leonid I

2018년 10월 1일

Maybe this would be difficult to implement in a time-constrained course, but it would be nice to have more insight into inner workings of various algorithms... Because otherwise this course resembles botanics.

교육 기관: Andres M L

2020년 12월 8일

I loved the course. The explanations are simple and full of day to day life examples. The final assignment was based on a real world problem, showing how the concepts can be applied not just in a play dataset

교육 기관: Vibhore G

2018년 2월 9일

From this course you will learn direct application of Machine Learning using python. You can dive into the world of machine learning. Ipython notebooks used are really helpful. Learned a lot from this course.

교육 기관: Eunis N

2020년 5월 20일

This course made me learn a lot machine learning techniques by experimenting them myself. It's more than just watching the class videos and running the notebook. You need to be ready to get your hands dirty!

교육 기관: Yingkai

2019년 2월 14일

It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken. Strongly recommend for your knowledge and career advance. Great professor!

교육 기관: Tsuyoshi N

2018년 10월 13일

Excellent course. I liked the projects in this course to recap the theories that I learned in the lecture and examine the new knowledge that I learned by myself with reading python library documents online.

교육 기관: Amir A B

2021년 9월 6일

Well-organized and useful course. The quizzes and programming assignments held at the end of each week make it practical and help to develop one’s problem-solving skills on a real-world dataset. Thank you.

교육 기관: Alexandre M

2019년 2월 1일

Good class, and it's very nice to have the "applied" machine learning angle (as opposed to focusing on the mathematical / theoretical underpinnings, which are only important at a much later point in time)

교육 기관: Josh B

2018년 2월 4일

Excellent introductory course to machine learning using python. It covers the basics for the popular supervised machine learning algorithms. I'm excited to build on the knowledge this course has given me.

교육 기관: NoneLand

2018년 1월 21일

A very practical course for machine learning. By this course, one can get familiar with sklearn and pandas basic operation! The last assignment is a challenge for me. Thanks teacher for this great course!

교육 기관: Dongliang Z

2017년 12월 21일

Very good lecture for beginner:easy to understand.

Also good assignment: force you to use what you learned in the course.

The discussion forum is helpful when you meet difficulties in assignments and quiz.

교육 기관: Steven L

2018년 4월 8일

Very practical introduction to using Python for machine learning - less focused on theory and more focused on how to use the sklearn library and proper use cases for different classifiers and regressors.

교육 기관: Carlos D R

2019년 12월 16일

The course offers you a lots fot tools the face ML problems. There are few errors in the notebooks, but everyting is well documented in the forum. Good overview to represent data and train basic models.

교육 기관: Giorgio C

2017년 8월 25일

The course is well structured and covers all the most important topics. The programming assignment could be a bit more stimulating. Overall I'd recommend this course to everyone who's interested in ML.

교육 기관: Ewa L

2017년 6월 17일

Fantastic course! Great foundation on scikit-learn. Really focused on APPLYING machine learning with just enough information about the models themselves to understand what's going on behind the scenes.

교육 기관: Eduardo B

2020년 7월 19일

Pretty good for those who are not too familiar with all the statistics that happens "under the hood" in a machine learning algorithm. The name "applied" suits very well in that way. Congratulations!

교육 기관: Angelo S

2018년 12월 20일

An excellent resource to immerse yourself into machine learning methods. Professor Kevyn explains key concepts in the most intuitive way possible. It does require some previous experience in Python.

교육 기관: Fernando T

2021년 4월 28일

Complete course about (mainly) supervised machine learning. Several classifiers and regressors and explained and compared. Appropiate assignments to test the understanding. I recommend this course.

교육 기관: Shrey S

2021년 11월 7일

Great experience i learned a lot in machine learning in python with different terminologies used in applied machine learning. I understand each and every topic which was told by Kevin Collins Sir!

교육 기관: Petko S

2018년 4월 3일

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!