Machine Learning Quick Reference

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 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 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 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.

Table of Contents

1. Quantifying Learning Algorithms
2. Evaluating Kernel Learning
3. Performance in Ensemble Learning
4. Training Neural Networks
5. Time-Series Analysis
6. Natural Language Processing
7. Temporal and Sequential Pattern Discovery
8. Probabilistic Graphical Models
9. Selected Topics in Deep Learning
10. Causal Inference
11. Advanced Methods