Book Description
Your handson 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 Description
Machine 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 realworld 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, timeseries, 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 crossvalidation
 Choose the best machine learning algorithm to solve your problem
 Explore kernel learning, neural networks, and timeseries 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 for
If 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.
Publisher Resources
Table of Contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface

Quantifying Learning Algorithms
 Statistical models
 Learning curve
 Curve fitting
 Statistical modeling – the two cultures of Leo Breiman
 Training data development data – test data
 Biasvariance trade off
 Regularization
 Crossvalidation and model selection
 Model selection using crossvalidation
 0.632 rule in bootstrapping
 Model evaluation
 Receiver operating characteristic curve
 Hmeasure
 Dimensionality reduction
 Summary
 Evaluating Kernel Learning
 Performance in Ensemble Learning
 Training Neural Networks
 Time Series Analysis
 Natural Language Processing
 Temporal and Sequential Pattern Discovery
 Probabilistic Graphical Models
 Selected Topics in Deep Learning
 Causal Inference
 Advanced Methods
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Product Information
 Title: Machine Learning Quick Reference
 Author(s):
 Release date: January 2019
 Publisher(s): Packt Publishing
 ISBN: 9781788830577