Master in Machine Learning with these Books

By ridhigrg |Email | Dec 31, 2019 | 2067 Views

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st Edition
by Drew Conway  
If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

  • Develop a nave Bayesian classifier to determine if an email is a spam, based only on its text
  • Use linear regression to predict the number of page views for the top 1,000 websites
  • Learn optimization techniques by attempting to break a simple letter cipher
  • Compare and contrast U.S. Senators statistically, based on their voting records
  • Build a "whom to follow" recommendation system from Twitter data

Machine Learning: An Algorithmic Perspective (Chapman & Hall/CRC Machine Learning & Pattern Recognition) Paperback - Import, 8 Apr 2009
by Stephen Marsland
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that makeup machine learning methods but also provides the background needed to understand how and why these algorithms work. 

Theory Backed up by Practical Examples
The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test the understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.

Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning 1st Edition, Kindle Edition
by Giuseppe Bonaccorso
Build a strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

Key Features
  • Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
  • Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
  • Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.
Book Description
  • 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.

Source: HOB