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Applied Machine Learning in Python に戻る

ミシガン大学(University of Michigan) による Applied Machine Learning in Python の受講者のレビューおよびフィードバック



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....



Oct 14, 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!!


Sep 09, 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


Applied Machine Learning in Python: 126 - 150 / 738 レビュー

by Dibyendu C

Oct 17, 2018

Well structured and quality lectures and assignments

by Navid A E

Oct 16, 2018

Absolutely the best professor ever!

by Jay N

Oct 18, 2018

very very excellent, got to learn whole lot of machine learning models and approaches. i'm straight away going for kaggle competitions after this.

by Ayon B

Oct 19, 2018

Good course. And challenging indeed, especially the quizzes.

by Ankur K

Sep 13, 2018


by Saumya S

Sep 12, 2018


by Jens L

Aug 20, 2018

Concise and clear presentation of the material with the majority of time focused around using TDD to learn and practice concepts through developing solutions to open ended coding challenges.

by Sylvain D

Sep 17, 2018

Great review of ML

by 李向杰

Sep 19, 2018

quite good, through this course I have gained knowledge and basic concept of machine learning and how to use python to run these machine learning models.

by Mandyam S

Sep 19, 2018

Quiz material were great!

by Noureddine B

Sep 18, 2018

Excellent course.

by Carlos F P

Sep 20, 2018

It gives a great overview of different machine learning methods. I found useful information that can be missing in other ML courses. Great course!

by Shreyas M T

Sep 22, 2018

Everything builds up very nicely on top of each other. A qualm some might have is that part of the assessments might be very simple. However, this is an applied course and the course material stays true to what it promises.

by Kedar J

Sep 25, 2018

Great course filled with a lot of details. The course does a great job in teaching all the important concepts. I felt the feature engineering should have been a dedicated topic. I got a lot of hints from the discussion forum and surprisingly there are even more concepts you have to learn for building a pipeline, treating categorical and numeric features differently. Overall challenging week4 assignment gives you confidence to deal with real world problem.

by Pratyush L

Sep 28, 2018

The course gives a good overview of the concepts and a great paced programming assignments to understand the concepts.


Sep 30, 2018


by SagarSrinivas

Oct 01, 2018

Awesome. Worth it!.

by Leonid I

Oct 01, 2018

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.

by Muhammad A R

Sep 24, 2018

Covers most of the basic supervised Machine learning Algorithms in SciKit-Learn from application POV.

by Prince K

Oct 05, 2018

good course for beginner

by Taha R

Oct 05, 2018

Great class. Thank you!

by Nilesh I

Oct 05, 2018

Practical, I liked the evaluation part

by Jimut B P

Oct 08, 2018


by Krishna D

Dec 09, 2017

Excellent Course. Well presented and good organized python notebooks, quiz and assignments.

Enjoyed the project very much.

Looking forward for future classes

by Binil K

Jul 10, 2017

This is a very nice course in Applied Machine Learning. For getting the most out of it, it would be nice to have taken ML Specialization from Andrew Ng which will take a deep divce into the working of ML models or have good amount of knowledge in ML. Having familiar with ML concepts, you would find this course really useful.