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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

7,736 個評分
1,414 條評論


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....



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.


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!!


1351 - Applied Machine Learning in Python 的 1375 個評論(共 1,399 個)

創建者 Ankur P


Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

創建者 James F


Good overview of methods. A bit too intense at times though, may have been better to really focus on a couple of key concepts.

創建者 Om R


The course is great, but need certain improvement for assignments and quizzes. The facts should be checked multiple times.

創建者 Darshan S


Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

創建者 Mauricio A E G M


This course is not useful to learn from scratch, but has some good things, for example the final assignment.

創建者 Nikola G


Really didn't like the quiz parts of the course. If it was up to me I would do thorough revision of these.

創建者 Chirag S


The content was less informative and audio quality was poor. However, assignments are fun completing.

創建者 Rohit S


The online grader needs to be updated as there is constant error showing up though our code is right

創建者 Gilad A


The last assignment was super. apart for it, the assignments and the course were too easy

創建者 Sai P


There were a few corrections made during the videos which ended being quite confusing.

創建者 Philip L


The assignments are extremely difficult, professor is a bit dry during lectures.

創建者 Dileep K


Although content is really helpful, assignment part has many technical issues!

創建者 Sundeep S S


Only classification based ML is covered. Regression based ML is non-existant.

創建者 Iuri A N d A


It has potential, but the assignment evaluation had a lot to be fixed.

創建者 Pakin P


How can i pass without reading discuss about problem with notebook

創建者 Hao W


The homework is too easy to improve our understanding of ML

創建者 M S V V


Too much of information compressed within a short span.

創建者 José D A M


Too fast, yet too difficult. Needs deeper explanation.

創建者 Navoneel C


Nice and Informative but not practically effective

創建者 Priyanka v


if it is more detailedthen it will be more useful

創建者 Sameed K


have to figure out a lot of things on you own.

創建者 Andy S


It could have been better with more examples.

創建者 Shan J


The explanation could have been much better.

創建者 Sagar J


Good start but i was very boring later on.

創建者 Jeremy D


The topics were good, but too many were d