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Supervised Machine Learning with Python - Develop rich Python coding practices while exploring supervised machine learning (Paperback)
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Supervised Machine Learning with Python - Develop rich Python coding practices while exploring supervised machine learning (Paperback)
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Teach your machine to think for itself! Key Features Delve into
supervised learning and grasp how a machine learns from data
Implement popular machine learning algorithms from scratch,
developing a deep understanding along the way Explore some of the
most popular scientific and mathematical libraries in the Python
language Book DescriptionSupervised machine learning is used in a
wide range of sectors (such as finance, online advertising, and
analytics) because it allows you to train your system to make
pricing predictions, campaign adjustments, customer
recommendations, and much more while the system self-adjusts and
makes decisions on its own. As a result, it's crucial to know how a
machine "learns" under the hood. This book will guide you through
the implementation and nuances of many popular supervised machine
learning algorithms while facilitating a deep understanding along
the way. You'll embark on this journey with a quick overview and
see how supervised machine learning differs from unsupervised
learning. Next, we explore parametric models such as linear and
logistic regression, non-parametric methods such as decision trees,
and various clustering techniques to facilitate decision-making and
predictions. As we proceed, you'll work hands-on with recommender
systems, which are widely used by online companies to increase user
interaction and enrich shopping potential. Finally, you'll wrap up
with a brief foray into neural networks and transfer learning. By
the end of this book, you'll be equipped with hands-on techniques
and will have gained the practical know-how you need to quickly and
powerfully apply algorithms to new problems. What you will learn
Crack how a machine learns a concept and generalize its
understanding to new data Uncover the fundamental differences
between parametric and non-parametric models Implement and grok
several well-known supervised learning algorithms from scratch Work
with models in domains such as ecommerce and marketing Expand your
expertise and use various algorithms such as regression, decision
trees, and clustering Build your own models capable of making
predictions Delve into the most popular approaches in deep learning
such as transfer learning and neural networks Who this book is
forThis book is for aspiring machine learning developers who want
to get started with supervised learning. Intermediate knowledge of
Python programming-and some fundamental knowledge of supervised
learning-are expected.
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