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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

7,144 個評分
1,296 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!!


1251 - Applied Machine Learning in Python 的 1275 個評論(共 1,276 個)

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 Pakin S


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 Jeremy D


The topics were good, but too many were d

創建者 Ryan S


Homeworks are inconvenient to submit



The narration was a bit boring.

創建者 shreyas


Teacher wasn't very good

創建者 Abir H R


very long videos

創建者 Wojciech G


To fast paced.

創建者 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 .

創建者 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

創建者 David C


Not as good as prev. courses. Univ. of mic. should update or get ride of this module

創建者 Aditya M


Can't the lecturer use proper slides with proper diagrams for a better explanation.



Things in the beginning are not explained properly

創建者 Konark Y


many issues while submitting assignments