Hands-On C++ Machine Learning for Beginners

Hands-On C++ Machine Learning for Beginners: Explore the fundamentals of Machine Learning in C++

Paperback Published on: 30/12/2018
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Synopsis

Up your algorithm building game by using C++ to predict and cluster data.
About This Book
* A quick guide to mastering machine learning using C++
* Understand linear regression and K-means clustering and their benefits and pitfalls
* Implement linear regression and K-means clustering
Who This Book Is For
This book is designed for data scientists, data analysts, and students who are competent in basic statistics and mathematical techniques and are looking for an introduction to the machine learning domain using C++. This book will provide a concrete foundation for future progression. You will be programming in C++, and hence some experience and understanding of OOP are required.
What You Will Learn
* Start your machine learning journey with C++
* Understand the difference between generative and discriminative machine learning
* How unsupervised and supervised learning varies
* Explore the benefits of linear regression and logistic regression
* Implement a linear regression algorithm for modeling a problem
* Understand the difference between K-means and K-NN algorithms
* Implement a K-means algorithm for cluster analysis
In Detail
Machine learning (ML) has become a fundamental part of the 21st century; from Netflix recommendations to fraud detection, ML is ever-present in our daily lives. C++ is a very fast language when it comes to executing your code and is extensively used when your final models are being deployed.
This book starts off with a broad overview of machine learning and the varying methods associated with it. You will master data types, machine learning algorithms, and a simple classification task to provide you with a foundation. We then study two simple but effective algorithms to deepen your understanding and provide some practical experience. Specifically, the two algorithms that we will be investigating are linear regression and K-means clustering, using easy-to-follow examples.
By the end of this course, you will have mastered machine Learning basics right and will be able to build efficient algorithms to help you to predict and cluster data ranges.

Publisher information

  • Publisher: Packt Publishing Limited
  • ISBN: 9781789530285
  • Number of pages: 39
  • Dimensions: 235 x 191 mm

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