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Data Cleaning and Exploration with Machine Learning - Get to grips with machine learning techniques to achieve sparkling-clean data quickly (Paperback)
Loot Price: R1,062
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Data Cleaning and Exploration with Machine Learning - Get to grips with machine learning techniques to achieve sparkling-clean data quickly (Paperback)
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Explore supercharged machine learning techniques to take care of
your data laundry loads Key Features Learn how to prepare data for
machine learning processes Understand which algorithms are based on
prediction objectives and the properties of the data Explore how to
interpret and evaluate the results from machine learning Book
DescriptionMany individuals who know how to run machine learning
algorithms do not have a good sense of the statistical assumptions
they make and how to match the properties of the data to the
algorithm for the best results. As you start with this book, models
are carefully chosen to help you grasp the underlying data,
including in-feature importance and correlation, and the
distribution of features and targets. The first two parts of the
book introduce you to techniques for preparing data for ML
algorithms, without being bashful about using some ML techniques
for data cleaning, including anomaly detection and feature
selection. The book then helps you apply that knowledge to a wide
variety of ML tasks. You'll gain an understanding of popular
supervised and unsupervised algorithms, how to prepare data for
them, and how to evaluate them. Next, you'll build models and
understand the relationships in your data, as well as perform
cleaning and exploration tasks with that data. You'll make quick
progress in studying the distribution of variables, identifying
anomalies, and examining bivariate relationships, as you focus more
on the accuracy of predictions in this book. By the end of this
book, you'll be able to deal with complex data problems using
unsupervised ML algorithms like principal component analysis and
k-means clustering. What you will learn Explore essential data
cleaning and exploration techniques to be used before running the
most popular machine learning algorithms Understand how to perform
preprocessing and feature selection, and how to set up the data for
testing and validation Model continuous targets with supervised
learning algorithms Model binary and multiclass targets with
supervised learning algorithms Execute clustering and dimension
reduction with unsupervised learning algorithms Understand how to
use regression trees to model a continuous target Who this book is
forThis book is for professional data scientists, particularly
those in the first few years of their career, or more experienced
analysts who are relatively new to machine learning. Readers should
have prior knowledge of concepts in statistics typically taught in
an undergraduate introductory course as well as beginner-level
experience in manipulating data programmatically.
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