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Machine Learning for Finance: Principles and practice for financial insiders
Machine Learning for Finance: Principles and practice for financial insiders

A guide to advances in machine learning for financial professionals, with working Python code

Key Features

  • Explore advances in machine learning and how to put them to work in financial industries
  • Clear explanation and expert discussion of how machine learning works, with...
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

Explore and master the most important algorithms for solving complex machine learning problems.

Key Features

  • Discover high-performing machine learning algorithms and understand how they work in depth
  • One-stop solution to mastering supervised, unsupervised, and...
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud

Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book?Amazon, Microsoft, Google, and PythonAnywhere.

You will work through a series of common Python data science problems...

Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Classic Problems of Probability
Classic Problems of Probability

"Classic Problems of Probability" is the winner of the 2012 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence.

"A great book, one that I will certainly add to my personal library."
--Paul J. Nahin, Professor Emeritus of Electrical
...

Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
In theory, Bayesian statistics is very simple. The posterior is proportional to the prior times likelihood. This gives the shape of the posterior, but it is not a density so it cannot be used for inference. The exact scale factor needed to make this a density can be found only in a few special cases. For other cases, the scale...
Mastering Clojure Data Analysis
Mastering Clojure Data Analysis

Leverage the power and fl exibility of Clojure through this practical guide to data analysis

About This Book

  • Explore the concept of data analysis using established scientific methods combined with the powerful Clojure language
  • Master Naive Bayesian Classification, Benford's Law, and...
Data Mining: Foundations and Intelligent Paradigms: VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects
Data Mining: Foundations and Intelligent Paradigms: VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of...

Diagrammatic Reasoning in AI
Diagrammatic Reasoning in AI

This book is really the end product of over a decade of work, on and off, on diagrammatic reasoning in artificial intelligence (AI). In developing this book, I drew inspiration from a variety of sources: two experimental studies, the development of two prototype systems, an extensive literature review and analysis in AI,...

Case Studies in Bayesian Statistical Modelling and Analysis
Case Studies in Bayesian Statistical Modelling and Analysis

Provides an accessible foundation to Bayesian analysis using real world models

This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the...

Data Analysis and Graphics Using R: An Example-Based Approach
Data Analysis and Graphics Using R: An Example-Based Approach

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from...

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