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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4.6
4,338 個評分
752 個審閱

課程概述

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....

熱門審閱

FL

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!!

OA

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

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101 - Applied Machine Learning in Python 的 125 個評論(共 735 個)

創建者 Nitin K

Mar 11, 2019

Well structured course that gave a good insight on applying Machine learning to real life cases.

創建者 Qiaochu S

Mar 11, 2019

This course has been really helpful to me! All the contents we need to grasp each week were well-designed and the assignments are easy, interesting and enlightening.

創建者 Oliver O

Mar 11, 2019

Great course!

創建者 Anne E

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

創建者 Harsh S

Mar 10, 2019

Great content

創建者 Naman M

Feb 26, 2019

The Instructor is marvelous. The Assignments are amazing, The TA is really responsive. The content only for one month course was outstanding, my feedback would be to increase the amount of exercises(coding) and assignments, and make the course for 2 months.

創建者 Yu S

Jul 16, 2018

Good applied material to study along theoretical material!

創建者 Benjamin S

Jul 12, 2018

Great class. Final project was a little unclear, but forums were a great help.

創建者 Peter B

Jul 11, 2018

Kevyn is an absolute joy to learn from. His enthusiasm for the topic is contagious, and his explanations are clear. The course content is well curated, tested, and reinforced. At the end of this course I feel confident that I can *actually* apply machine learning to real world problems and competitions. This is not just a 'good' course, it's a new gold standard in e-learning.

創建者 Jan P

Jul 11, 2018

great course - I learnt a lot!

創建者 Vishesh G

Sep 08, 2018

This was an amazing course that I absolutely loved working on. It gave a deep insight into machine learning. I gained a lot of knowledge from this course. A must for the students who are just stepping in the field of Machine Learning.

創建者 Mai N

Sep 08, 2018

Good starting points for any machine learning folks

創建者 Mohit K

Sep 09, 2018

Wonderful Course.

創建者 Sunny K L W

Sep 08, 2018

Great Course with high practicality. Need more lectures on how to process categorical data. Read the Forum if you encounter any question!

創建者 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.

創建者 Muhammad A R

Sep 24, 2018

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

創建者 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.

創建者 Ankur K

Sep 13, 2018

Awesome

創建者 Saumya S

Sep 12, 2018

excellent

創建者 Pratyush L

Sep 28, 2018

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

創建者 Sylvain D

Sep 17, 2018

Great review of ML

創建者 Vasilis S

Aug 12, 2018

Great course! Assignment 4 is very interesting and allows you to apply all you've learnt in this course at once.

創建者 Sathvik K

Aug 28, 2018

great for learning how to practically apply machine learning

創建者 李子杰

Aug 30, 2018

Easy for beginner to follow. After finishing the course,I'm able to apply simple machine learning algorithms to area I'm currently working on

創建者 Ammar A M

Sep 02, 2018

One of the best ML courses on the platform. I highly recommend it to all data-science enthusiasts. It would be nice to have pandas data-wrangling skills before tackling the final project as it is a must. Totally enjoyed the final project! was a great learning experience seeing my classifier AUC going from 57 all the way to more than 76 and the impact of feature importance and cleaning on the model performance was eye-opener!