Beginning R: An Introduction to Statistical Programming
Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization,...
Unsupervised Learning with R
Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
About This Book
Unlock and discover how to tackle clusters of raw data through practical examples in R
Explore your data and create your own models from scratch...
Learning Geospatial Analysis with Python
If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.
Construct applications for GIS development by exploiting...
Practical Data Science with R
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language...
Practical Statistics for Data Scientists: 50 Essential Concepts
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their...
Predictive Analytics For Dummies
Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in...
Al Application Programming
The purpose of this book is to demystify the techniques associated with the field of artificial intelligence. It will cover a wide variety of techniques currently defined as "AI" and show how they can be useful in practical, everyday applications.
Many books on artificial intelligence provide tutorials for AI methods, but...
Character Recognition Systems: A Guide for Students and Practitioners "Much of pattern recognition theory and practice, including methods such as Support Vector Machines, has emerged in an attempt to solve the character recognition problem. This book is written by very well-known academics who have worked in the field for many years and have made significant and lasting contributions. The book will no doubt be... 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...
Principles of Data Mining (Adaptive Computation and Machine Learning) The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly... Machine Learning in Action
After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on...
Machine Learning with R - Second Edition
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
Machine learning, at its core, is concerned...
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