Your hands-on reference guide to developing, training, and
optimizing your machine learning models Key Features Your guide to
learning efficient machine learning processes from scratch Explore
expert techniques and hacks for a variety of machine learning
concepts Write effective code in R, Python, Scala, and Spark to
solve all your machine learning problems Book DescriptionMachine
learning makes it possible to learn about the unknowns and gain
hidden insights into your datasets by mastering many tools and
techniques. This book guides you to do just that in a very compact
manner. After giving a quick overview of what machine learning is
all about, Machine Learning Quick Reference jumps right into its
core algorithms and demonstrates how they can be applied to
real-world scenarios. From model evaluation to optimizing their
performance, this book will introduce you to the best practices in
machine learning. Furthermore, you will also look at the more
advanced aspects such as training neural networks and work with
different kinds of data, such as text, time-series, and sequential
data. Advanced methods and techniques such as causal inference,
deep Gaussian processes, and more are also covered. By the end of
this book, you will be able to train fast, accurate machine
learning models at your fingertips, which you can easily use as a
point of reference. What you will learn Get a quick rundown of
model selection, statistical modeling, and cross-validation Choose
the best machine learning algorithm to solve your problem Explore
kernel learning, neural networks, and time-series analysis Train
deep learning models and optimize them for maximum performance
Briefly cover Bayesian techniques and sentiment analysis in your
NLP solution Implement probabilistic graphical models and causal
inferences Measure and optimize the performance of your machine
learning models Who this book is forIf you're a machine learning
practitioner, data scientist, machine learning developer, or
engineer, this book will serve as a reference point in building
machine learning solutions. You will also find this book useful if
you're an intermediate machine learning developer or data scientist
looking for a quick, handy reference to all the concepts of machine
learning. You'll need some exposure to machine learning to get the
best out of this book.
General
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