by Stephen Marsland
There are two groups in which machine learning books are divided for the ones who are aiming 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. Machine Learning: An Algorithmic Perspective is that text.
Highlights a Range of Disciplines and Applications
Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.
by Henrik Brink (Author), Joseph Richards (Author), Mark Fetherolf (Author)
RealWorld Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
RealWorld Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You will also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you are done, you will be ready to successfully build, deploy, and maintain your own powerful ML systems.
by Peter Flach (Author)
As one of the most comprehensive machines learning texts around, this book does justice to the fields incredible richness, but without losing sight of the unifying principles. Peter Flachs clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorization and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
by Brett Lantz
Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R.
Harness the power of R for statistical computing and data science
Explore, forecast, and classify data with R
Use R to apply common machine learning algorithms to real-world scenarios
What you will learn
- Harness the power of R to build common machine learning algorithms with real-world data science applications
- Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results
- Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems
- Classify your data with Bayesian and nearest neighbor methods
- Predict values using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression and model your data with neural networks
- Evaluate and improve the performance of machine learning models
by Carl Edward Rasmussen
This book is a comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification and includes detailed algorithms.
The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.