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Pluralsight.Preparing.Data.for.Modeling.with.scikit-learn-XQZT
Preparing Data for Modeling with scikit-learn
561.2 MB
08/12/2019
Course # : N/A
Published : Aug 12 2019
Modified : N/A
URL : www.pluralsight.com/library/courses/preparing-data-modeling-scikit-learn
Author : Janani Ravi
Duration : 3h 40m
Skill : Advanced
Exer/Code : [X]
Description:
Even as the number of machine learning frameworks and libraries increases on a
daily basis, scikit-learn is retaining its popularity with ease. Scikit-learn
makes the common use-cases in machine learning - clustering, classification,
dimensionality reduction and regression - incredibly easy. In this course,
Preparing Data for Modeling with scikit-learn, you will gain the ability to
appropriately pre-process data, identify outliers and apply kernel
approximations. First, you will learn how pre-processing techniques such as
standardization and scaling help improve the efficacy of ML algorithms. Next,
you will discover how novelty and outlier detection is implemented in scikit-
learn. Then, you will understand the typical set of steps needed to work with
both text and image data in scikit-learn. Finally, you will round out your
knowledge by applying implicit and explicit kernel transformations to transform
data into higher dimensions. When you’re finished with this course, you will
have the skills and knowledge to identify the correct data pre-processing
technique for your use-case and detect outliers using theoretically robust
techniques.