Machine Learning Algorithms

Machine Learning Algorithms

Build strong foundation for entering the world of machine learning and data science with the help of this comprehensive guide

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

In this book, you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book, you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

On completion of the book, you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.

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About the Book

Who This Book Is For

This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

What You Will Learn

  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs for Linear Regression
  • Build a data model and understand how it works by using different types of algorithm
  • Learn to tune the parameters of Support Vector Machines
  • Implement clusters to a dataset
  • Explore the concept of Natural Processing Language and Recommendation Systems
  • Create a ML architecture from scratch.
Details
Author:
Series: Machine Learning Algorithms
Genres: Data Science, Machine Learning
Tags: Data Science, Machine Learning
Publisher: Packt Publishing
Publication Year: 2017
ASIN: B072QBG11J
ISBN: 9781785889622
About the Author
Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his MScEng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.

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