Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... This book will show you how to take advantage of TensorFlow’s most appealing features - simplicity, efficiency, and flexibility - in various scenarios. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field.This book is a series of seventeen edited “student-authored lectures” which explore in depth the core of data ... This is a continuation of the popular PHYSTAT series; previous meetings were held at CERN (2000), Fermilab (2000), Durham (2002) and Stanford (2003).In-depth discussions on topical issues are presented by leading statisticians and research ... This book demonstrates AI projects in Python covering modern techniques that make up the world of Artificial Intelligence. Found insideThis practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes. BioBranch will offer the ability to: (1) upload and share datasets intended for classification tasks, (2) construct decision trees by manually selecting features such as genes for a gene expression dataset, (3) collaboratively edit decision ... "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike. Found inside – Page iMany of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning. Found insideWho This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. The Handbook of Research on Machine Learning Innovations and Trends is a key resource on the latest advances and research regarding the vast range of advanced systems and applications involved in machine intelligence. Found insideThe main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, ... Found insideUsing clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning ... Found insideThis book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. This book explores a proactive and domain-driven method to classification tasks. Found insideThis Learning Path includes content from the following Packt products: Statistics for Machine Learning by Pratap Dangeti Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim Pandas Cookbook by Theodore Petrou What you will learn ... Presents a unified, efficient model of random decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images, automatic diagnosis from radiological scans and document ... This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. In this book, we'll show you how to incorporate various machine learning libraries available for iOS developers. You’ll quickly get acquainted with the machine learning fundamentals and implement various algorithms with Swift. Found insideA walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer: Found inside – Page 1The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. "Learn how to use decision trees and random forests for classification and regression, their respective limitations, and how the algorithms that build them work. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. This book is dedicated to the introduction and explanation of its data analysis power and focused in decision trees. This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. Found inside – Page 115In: NIPS, pp 882–888 Piccarreta R (2008) Classification trees for ordinal ... Public: a decision tree classifier that integrates building and pruning. In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. Found insideThis volume focuses on Nature Inspired Computing. The contents of this book will be useful to researchers and students alike. This volume comprises the select proceedings of the annual convention of the Computer Society of India. This book comprises selected papers of the International Conferences, DTA and BSBT 2011, held as Part of the Future Generation Information Technology Conference, FGIT 2011, in Conjunction with GDC 2011, Jeju Island, Korea, in December 2011. The topics covered in this book are: -An overview of decision trees and random forests -A manual example of how a human would classify a dataset, compared to how a decision tree would work -How a decision tree works, and why it is prone to ... Found insideLearn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book Break text down into its component parts for spelling correction, feature extraction, and phrase transformation Work through NLP ... Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... Found insideThis book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Found insideThis book is about making machine learning models and their decisions interpretable. Abstract: "This paper presents a survey of decision tree classifiers (DTC's), which have been under study in the last two decades; the overall approaches are pointed out with their possible shortcomings and/or advantages. The book will help you get well-versed with different techniques in Artificial Intelligence such as machine learning, deep learning, natural language processing and more to build smart IoT systems. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. 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