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* Title..: Pluralsight Reducing Dimensions in Data with scikit-learn
* Date...: 19-04-2019
* Type...: Bookware
* Disks..: 20x15mb

Release notes:

Dimensionality Reduction is a powerful and versatile machine
learning technique that can be used to improve the
performance of virtually every ML model. Using
dimensionality reduction, you can significantly speed up
model training and validation, saving both time and money,
as well as greatly reduce the risk of overfitting.

In this
course, Reducing Dimensions in Data with scikit-learn, you
will gain the ability to design and implement an exhaustive
array of feature selection and dimensionality reduction
techniques in scikit-learn.

First, you will learn the
importance of dimensionality reduction, and understand the
pitfalls of working with data of excessively high-
dimensionality, often referred to as the curse of
dimensionality.

Next, you will discover how to implement
feature selection techniques to decide which subset of the
existing features we might choose to use, while losing as
little information from the original, full dataset as
possible.

You will then learn important techniques for
reducing dimensionality in linear data. Such techniques,
notably Principal Components Analysis and Linear
Discriminant Analysis, seek to re-orient the original data
using new, optimized axes. The choice of these axes is
driven by numeric procedures such as Eigenvalue and Singular
Value Decomposition.

You will then move to dealing with
manifold data, which is non-linear and often takes the form
of swiss rolls and S-curves. Such data presents an illusion
of complexity, but is actually easily simplified by
unrolling the manifold. Finally, you will explore how to
implement a wide variety of manifold learning techniques
including multi-dimensional scaling (MDS), isomap, and t-
distributed Stochastic Neighbor Embedding (t-SNE). You will
round out the course by comparing the results of these
manifold unrolling techniques with different datasets,
including images of faces and handwritten data.

When
you're finished with this course, you will have the skills
and knowledge of Dimensionality Reduction needed to design
and implement ways to mitigate the curse of dimensionality
in scikit-learn.

More info:
https://www.pluralsight.com

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