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Machine Learning in Python - Essential Techniques for Predictive Analysis (Paperback)
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Machine Learning in Python - Essential Techniques for Predictive Analysis (Paperback)
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Learn a simpler and more effective way to analyze data and predict
outcomes with Python Machine Learning in Python shows you how to
successfully analyze data using only two core machine learning
algorithms, and how to apply them using Python. By focusing on two
algorithm families that effectively predict outcomes, this book is
able to provide full descriptions of the mechanisms at work, and
the examples that illustrate the machinery with specific, hackable
code. The algorithms are explained in simple terms with no complex
math and applied using Python, with guidance on algorithm
selection, data preparation, and using the trained models in
practice. You will learn a core set of Python programming
techniques, various methods of building predictive models, and how
to measure the performance of each model to ensure that the right
one is used. The chapters on penalized linear regression and
ensemble methods dive deep into each of the algorithms, and you can
use the sample code in the book to develop your own data analysis
solutions. Machine learning algorithms are at the core of data
analytics and visualization. In the past, these methods required a
deep background in math and statistics, often in combination with
the specialized R programming language. This book demonstrates how
machine learning can be implemented using the more widely used and
accessible Python programming language. * Predict outcomes using
linear and ensemble algorithm families * Build predictive models
that solve a range of simple and complex problems * Apply core
machine learning algorithms using Python * Use sample code directly
to build custom solutions Machine learning doesn't have to be
complex and highly specialized. Python makes this technology more
accessible to a much wider audience, using methods that are
simpler, effective, and well tested. Machine Learning in Python
shows you how to do this, without requiring an extensive background
in math or statistics.
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