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學生對 IBM 提供的 Supervised Machine Learning: Classification 的評價和反饋

116 個評分
28 條評論


This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics....



Superb ,detailed, well explained, lots of hands on training through labs and most of the major alogrithms are covered!\n\nKeep up the good work. You guys are helping the community a lot :D


The course is very well structured, and the explanations very clear. I would only suggest enhancing the peer-review community since it takes a long time to get a review sometimes.


26 - Supervised Machine Learning: Classification 的 29 個評論(共 29 個)

創建者 Hossam G M


The course content is very great in the coding area and it is very helping. but a shortage that is clear is the theory behind every algorithm, the handling of it wasn't that much perfect.

創建者 Cristiano C


Interesting Course, sometimes it skips some arguments that should be, imho, studied a bit deeper (i.e. UP/DOWN sampling), for the rest it's a great course with a great teacher!

創建者 Keyur U


This course is has a detailed explanation on each and every aspect of classification.

創建者 Meith N


Need to cover some basic information and examples too cause directly start from complex examples in the code section