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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

7,973 個評分
1,452 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....




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!!



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


551 - Applied Machine Learning in Python 的 575 個評論(共 1,442 個)

創建者 Max S


One of the best courses ever! Plenty of things to learn, to evolve. Superb!

創建者 Phat N


Very good introductory course to approach scikit_learn!! Highly Recommend!!

創建者 Lucas P G


Very good starting point to learn how to apply the algorithims of sklearn.

創建者 Evgeny C


Really good and insightful course, but auto grader needs some improvement.

創建者 Dr G N R


excellent . but somewhat twisty assignments. please give easy assignments

創建者 Bruce M


Excellent course. I learned a lot and enjoyed the challenging


創建者 Mohammad S


really helpful course that acts like a great refresher before interviews.

創建者 Sacenda C


Incredible MOOC ! Very interesting if you want to start machine learning.

創建者 Husam A


The best course to survey machine learning algorithms and methodologies.

創建者 Anirudh S


Fantastically explained. with jupyter notebooks! appreciate your effort.

創建者 Ahmad H S


It is Comprehensive course and give great baseline to enter this domain

創建者 Val A B


This is a very good way to start machine learning if you're a beginner.

創建者 Holger P


Great course covering Python's Machine Learning library scikit-learn.

創建者 陈明


This is really a very practical course, and inspiring me to learn more

創建者 Qian H


Nice course with an good level. I really leant a lot from this course!

創建者 Thịnh C


I learnt a lot from this course. 10/10 would recommend to my friends.

創建者 Tanmoy M


Amazing. How can a course be so awesome!!! Thanks to the Instructors.

創建者 PURNA C R . K


It's a superb course well organised with good and real time examples.

創建者 Weinan H


A very systematic introduction to most used machine learning models.

創建者 Γεώργιος Κ


A must to to have lesson for Data Science using Pandas and Matplotlib

創建者 mugnaio


As all course in this specialization, this is very interesting course

創建者 Oscar J O R


Amazing module. Clear explanations and useful examples and exercises.

創建者 Deep S


Precise and concise. Good for getting started in using Python for ML

創建者 devanshu s


very good course! Had great time applying the concepts I had learnt.

創建者 Bina S


Learned Machine learning enough to know what it is and how it works.