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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....



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!!


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.


Applied Machine Learning in Python: 1301 - 1325 / 1,334 レビュー

by Sai P


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

by Philip L


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

by Sundeep S S


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

by Pakin P


How can i pass without reading discuss about problem with notebook

by Hao W


The homework is too easy to improve our understanding of ML

by M S V V


Too much of information compressed within a short span.

by José D A M


Too fast, yet too difficult. Needs deeper explanation.

by Navoneel C


Nice and Informative but not practically effective

by Priyanka v


if it is more detailedthen it will be more useful

by Sameed K


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

by Andy S


It could have been better with more examples.

by Shan J


The explanation could have been much better.

by Sagar J


Good start but i was very boring later on.

by Jeremy D


The topics were good, but too many were d

by Ryan S


Homeworks are inconvenient to submit



The narration was a bit boring.

by shreyas


Teacher wasn't very good

by Abir H R


very long videos

by Wojciech G


To fast paced.

by Aarya P


Really disappointed with the course may ask why??

The first thing is the instructor , super boring. The instructor (with all due respect) was very dry and the lectures were super uninteresting. When he keeps on talking code, but doesn't really explain stuff. The material and lectures were dry and colorless.

Me without having good statistics background had huge difficulties understanding the concepts. Please i recommend everyone to have good knowledge in statistics before starting the course. ABSOLUTELY NOT THE BEGINNER LEVEL AND NEITHER INTERMIDIATE LEVEL .the course is quiteeeee difficult.

You also need to have a lot of self study , which i am not a big fan of. I hope they make the course more fun rather than a man constantly talking on the screen .

by Daniel J


I found this course quite challenging to complete. The assignments are difficult (which is good, they are practical and I enjoyed them) and only a fraction of things is explained in the videos. I really found much better learning materials around the web (and for free!). For applied machine learning course, I would expect more practical videos. Also the process of submitting assignments is really frustrating, I spent half the time correcting errors that were not related to the assignment objective. If this course was not part of specialization, I would not complete it.

by Douglas H


Lectures are good but they expect you to extract too many fine details from them in order to pass the quizzes and assignments. You'd have to watch these oral lessons ten times in order to pass the tests, which are needlessly nitpicky.

by Oswaldo C


Los videos no son suficientemente extensos ni para explicar el código, ni para explicar la teoría detrás de los algoritmos, se queda a medio camino de los dos siendo insuficiente en ambos casos

by Jean-Michel P


The better course of this stack... and that's all the positive feedback I have. This course is still very poorly designed and unstructured with a bunch of unfixed mistakes after 4+ years.

by Vjaceslavs M


This course is outdated by few years and not been updated in general with lots of mistakes in assignments and on slides making it very not ejoyable to use.