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eBook

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Download this book in **EPUB** and **PDF** formats

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Publication date :
Jan 31, 2019

Length
294 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781788830577

Category :

Languages :

Concepts :

- 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

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.

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

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Jan 31, 2019

Length
294 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781788830577

Category :

Languages :

Concepts :

Title Page

Copyright and Credits

About Packt

Contributors

Preface

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

1. Other Books You May Enjoy

Index

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