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JUDGEMENT

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* Title..: Pluralsight Building Features from Numeric Data
* Date...: 09-04-2019
* Type...: Bookware
* Disks..: 16x15mb

Release notes:

The quality of preprocessing that numeric data is subjected
to is an important determinant of the results of machine
learning models built using that data. With smart, optimized
data pre-processing, you can significantly speed up model
training and validation, saving both time and money, as well
as greatly improve model performance in prediction.

this course, Building Features from Numeric Data, you will
gain the ability to design and implement effective,
mathematically sound data pre-processing pipelines.

First,
you will learn the importance of normalization,
standardization and scaling, and understand the intuition
and mechanics of tweaking the central tendency as well as
dispersion of a data feature.

Next, you will discover how
to identify and deal with outliers and possibly anomalous
data. You will then learn important techniques for scaling
and normalization. Such techniques, notably normalization
using the L1-norm, L2-norm and Max norm, seek to transform
feature vectors to have uniform magnitude. Such techniques
find wide usage in ML model building - for instance in
computing the cosine similarity of document vectors, and in
transforming images before techniques such as convolutional
neural networks are applied to them.

You will then move
from normalization and standardization to scaling and
transforming data. Such transformations include quantization
as well as the construction of custom transformers for
bespoke use cases. Finally, you will explore how to
implement log and power transformations. You will round out
the course by comparing the results of three important
transformations - the Yeo-Johnson transform, the Box-Cox
transform and the quantile transformation - in converting
data with non-normal characteristics, such as chi-squared or
lognormal data into the familiar bell curve shape that many
models work best with.

When you're finished with this
course, you will have the skills and knowledge of data
preprocessing and transformation needed to get the best out
of your machine learning models.

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

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