




Think Stats
If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
You'll work with a case study throughout the...   Machine Learning with R  Second Edition
Key Features

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 realworld scenarios
Book Description
Machine learning, at its core, is concerned...   Heuristic and Optimization for Knowledge DiscoveryWith the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic... 

Bayesian Reasoning and Machine Learning
We live in a world that is rich in data, ever increasing in scale. This data comes from many dierent
sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer
databases, nancial transactions, engine monitoring, speech recognition, surveillance, search). Possessing
the knowledge as to...     Mathematical Statistics with Applications
This textbook is of an interdisciplinary nature and is designed for a two or onesemester course in
probability and statistics, with basic calculus as a prerequisite. The book is primarily written to give
a sound theoretical introduction to statistics while emphasizing applications. If teaching statistics
is the main purpose of a... 



Result Page: 11 10 9 8 7 6 5 4 3 2 