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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Python: Data Analytics and Visualization
Python: Data Analytics and Visualization

Understand, evaluate, and visualize data About This Book - Learn basic steps of data analysis and how to use Python and its packages - A step-by-step guide to predictive modeling including tips, tricks, and best practices - Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for...

Kernel Smoothing: Principles, Methods and Applications
Kernel Smoothing: Principles, Methods and Applications

Comprehensive theoretical overview of kernel smoothing methods with motivating examples

Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of...

Using R for Data Management, Statistical Analysis, and Graphics
Using R for Data Management, Statistical Analysis, and Graphics

Quick and Easy Access to Key Elements of Documentation
Includes worked examples across a wide variety of applications, tasks, and graphics

Using R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to...

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)―classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is...

Machine Learning Projects for .NET Developers
Machine Learning Projects for .NET Developers

Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a...

Introduction to Machine Learning with R: Rigorous Mathematical Analysis
Introduction to Machine Learning with R: Rigorous Mathematical Analysis

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more...

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data....

Probability and Statistics for Computer Scientists
Probability and Statistics for Computer Scientists

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic
...

Data Mining and Business Analytics with R
Data Mining and Business Analytics with R

Collecting, analyzing, and extracting valuable information froma large amount of data requires easily accessible, robust,computational and analytical tools. Data Mining and BusinessAnalytics with R utilizes the open source software R for theanalysis, exploration, and simplification of large high-dimensionaldata sets. As a result, readers are...

Practical Statistics for Data Scientists: 50 Essential Concepts
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...

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