This book contains a fast-paced introduction to data-related tasks in preparation for training models ondatasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset. Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading.FEATURES: Covers extensive topics related to cleaning datasets and working with models Includes Python-based code samples and  a separate chapter on Matplotlib and Seaborn Features companion files with source code, datasets, and figures from the book

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing using R. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The final chapter presents the Transformer Architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years. Companion files with source code and figures are included. Features Covers extensive topics related to natural language processing using R Features companion files with source code and figures from the book

Introduces an assortment of powerful command line utilities that can be combined to create simple, yet powerful shell scripts for processing datasets. The code samples and scripts use the bash shell, and typically involve small datasets so you can focus on understanding the features of grep, sed, and awk. Companion files with code are available for downloading from the publisher.

As part of the best-selling Pocket Primer series, this book is designed to provide an introduction to Python tools which are used by data scientists. It includes coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. The first chapter contains a quick tour of basic Python, followed by a chapter introducing NumPy, and a chapter on Pandas. Chapter 4 provides a high-level view of Sklearn and SciPy. Chapter 5 contains an assortment of data cleaning tasks that are solved via Python and the awk programming language. Chapter 6 delves into data visualization with Matplotlib, Seaborn, and Bokeh. Next, one appendix explores issues that can arise with data, followed by an appendix on awk. Numerous code samples are used to illustrate concepts. Companion files with source code are available for downloading from the publisher. FEATURES: Introduces Python, NumPy, Sklearn, SciPy, and awk. Covers data cleaning tasks and data visualization. Features numerous code samples throughout. Includes companion files with source code .

This book is for developers who are looking for an introduction to basic concepts in NLP and machine learning. Numerous code samples and listings are included to support myriad topics. The first two chapters contain introductory material for NumPy and Pandas, followed by chapters on NLP concepts, algorithms and toolkits, machine learning, and NLP applications. The final chapters include examples of NLP tasks using TF2 and Keras, the Transformer architecture, BERT-based models, and the GPT family of models. The appendices contain introductory material (including Python code samples) for various topics, including data and statistics, Python3, regular expressions, Keras, TF2, Matplotlib and Seaborn. Companion files with source code and figures are included. FEATURES * Covers extensive topics related to natural language processing and machine learning * Includes separate appendices on data and statistics, regular expressions, data visualization, Python, Keras, TF2, and more * Features companion files with source code and color figures from the book

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA ("state of the art"). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included. FEATURES: Covers extensive topics related to natural language processing Includes separate appendices on regular expressions and probability/statistics Features companion files with source code and figures from the book.

This book is intended primarily for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks. It contains a variety of features of NumPy and Pandas and how to create databases and tables in MySQL. Chapter 7 covers many data wrangling tasks using Python scripts and awk-based shell scripts. Companion files with code are available for downloading from the publisher. FEATURES: Provides the reader with basic Python 3, Java, and Pandas programming concepts, and an introduction to awk. Includes a chapter on RDBMs and SQL. Companion files with code.

As part of the best-selling Pocket Primer series, this book is meant to familiarize readers with regular expressions as part of their data-related tasks. Regular expressions (regex) are used in search engines, search and replace dialogs of word processors and text editors, in text processing utilities such as sed and awk, and in lexical analysis. Many programming languages provide regex capabilities, either built-in or via libraries. Readers will learn how to use bash commands grep, awk, and sed in order to use regular expressions in conjunction with datasets and data-related tasks. Companion files with source code are available for downloading from the publisher. Features: A practical introduction to using flexible, bash commands to perform common data cleaning tasks. Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks. Simple yet powerful shell scripts for data cleaning tasks.

As part of the best-selling PocketPrimer series, this book is designed tointroduce readers to an assortment of powerful command-line utilities that canbe combined to create simple, yet powerful shell scripts. While all examplesand scripts use the "bash" command set, many of the concepts translate intoother command shells (such as sh, ksh,zsh, and csh), including the concept of piping data between commands, regular expression substitution, and the sed and awk commands. Aimed at a reader relatively new to working in a bash environment, the book is comprehensive enough to be a good reference and teach a few new techniques to those who already have some experience with creating shell scripts. It contains a variety of code fragments and shell scripts for data scientists, data analysts, and other people who want shell-based solutions to"clean" various types of text files. In addition, the concepts and code samplesin this book are useful for people who want to simplify routine tasks. Features: Takes introductory concepts and commands in bash, and then demonstrates their uses in simple yet powerful shell scripts Contains a variety of code fragments and shell scripts for data scientists, data analysts, and other people who wantshell-based solutions to "clean" various types of text files

As part of the best-selling Pocket Primer series, this book is designed to present the fundamentals of data structures using Python. Data structures provide a means to managing huge amounts of information such as large databases and the ability to use search and sort algorithms effectively. It is intended to be a fast-paced introduction to the core concepts of Python and data structures, illustrated with numerous code samples. Companion files with source code are available for downloading.

As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to Java development for people who are relatively new to the Java programming language. It is intended to be a fast-paced introduction to the core concepts of Java and Java APIs, illustrated with code samples using primarily Java 8. Companion files with source code are available. FEATURES: Covers Boolean logic, loops, arrays, recursion, OOP concepts, data structures, streams, SQL, and more. Lists new features in Java 9 through Java 13. Features numerous code samples throughout. Includes companion files with source code.

As part of the best-selling Pocket Primer series, this book is an effort to give programmers sufficient knowledge of data cleaning to be able to work on their own projects. It is designed as a practical introduction to using flexible, powerful (and free) Unix/Linux shell commands to perform common data cleaning tasks. The book is packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Companion files with source code are available for downloading from the publisher. FEATURES: A practical introduction to using flexible, powerful (and free) Unix/Linux shell commands to perform common data cleaning tasks. Includes the concept of piping data between commands, regular expression substitution, and the 'sed' and 'awk' commands. Packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks.

As part of the best-selling Pocket Primer series, this book is primarily for data scientists and machine learning engineers who want to expand their current knowledge of SQL using MySQL as the primary RDBMS. It includes Python-based code samples to access data from a MySQL table in a Pandas data frame and Java-based code samples for accessing data in a MySQL database, along with XML documents and JSON documents. The book also introduces NoSQL, presents an overview of MongoDB, and SQLite--an open-source RDBMS availableon mobile devices. The final chapter of the book covers a diverse set of miscellaneous topics, such as normalization, schemas, database optimization, and performance. Numerous code samples and listings are included to support myriad topics. Companion files with source code and figures are available from the publisher. FEATURES: Covers extensive topics related to SQL, using MySQL as the primary RDBMS Introduces NoSQL, presents an overview of MongoDB, and SQLite--an open-source RDBMS available on mobile devices Features companion files with source code and figures from the book

As part of the best-selling Pocket Primer Series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 and linear algebra Provides a thorough introduction to data visualization and regular expressions Covers NumPy, Pandas, R, and SQL Introduces probability and statistical concepts Features numerous code samples throughout Includes companion files with source code and figures

Houston, we have a problem... Flash cannot run on iPhone. Good news, though...Flash can and will run on Android Android will give you what you need to continue building cool and fresh Flash-based Apps for mobile devices, smartphones, and more. Pro Android Flash is the definitive guide to building Flash and other rich Internet apps (RIAs) on the Android platform. It covers the most popular RIA frameworks for Android developers - Flex and JavaFX - and shows you how to build rich, immersive user experiences on both Android smartphones and tablets. You'll learn how to incorporate multimedia, animation, and special effects into your apps for maximum visual appeal. You'll also cover advanced topics, including input methods, hardware inputs, deployment, and performance optimization.What you'll learn * Deployment of Flash, Flex, and JavaFX to Android * How to take your desktop RIAs and adapt them for mobile devices * How to integrate hardware inputs from the camera, GPS, compass, and accelerometer * How to build an immersive user interface with audio and video assets * How to integrate styling and artifact s from a professional designer * Best practices for mobile performance tuning and optimization Who this book is for This book is intended for developers who are looking to build rich Internet applications (RIAs) for the Android platform, especially Flash, Flex, and JavaFX.

This book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. It contains a variety of code samples and features of NumPy and Pandas, and how to write regular expressions. Chapter 3 includes fundamental statistical concepts and Chapter 7 covers data visualization with Matplotlib and Seaborn. Companion files with code are available for downloading from the publisher.

The first three chapters of this book contain a short tour of basic Angular functionality, such as UI components and forms in Angular applications. The fourth chapter introduces you to machine learning concepts, such as supervised and unsupervised learning, followed by major types of machine learning algorithms (regression, classification, and clustering), along with a section regarding linear regression. The fifth chapter is devoted to classification algorithms, such as kNN, Naive Bayes, decision trees, random forests, and SVM (Support Vector Machines). The sixth chapter introduces basic TensorFlow concepts, followed by tensorflowjs (i.e., TensorFlow in modern browsers), and some examples of Angular applications combined with machine learning. In addition, this book contains an appendix for deep learning.

This book is intended primarily for developers who have little or no experience with Python or Pandas. It contains a fast-paced introduction to Python and Python-based solutions to various tasks. Chapter 1 provides a quick tour of basic Python 3, followed by a chapter that shows how to work with loops and conditional logic in Python. Chapter 3 discusses data structures in Python, followed by a chapter that features code samples for tasks with strings and arrays in Python. Chapter 5 contains concepts in object-oriented programming, along with code samples that illustrate how they are implemented in Python. Chapter 6 introduces recursion and some fundamental topics in combinatorics. Finally, the appendix provides an introduction to Pandas. Companion files with code and figures are available for downloading from the publisher. Features: Provides the reader with basic Python 3 and Pandas programming concepts. Companion files with code and figures

This book provides readers with enough information for them to develop more sophisticated Angular applications that incorporate deep learning. The first three chapters of this book contain a short tour of basic Angular functionality, such as UI components and forms in Angular applications. The fourth chapter introduces you to deep learning, the problems it can solve, and some challenges for the future. You will also learn about MLPs (Multi Layer Perceptrons), CNNs (Convolutional Neural Networks), and a Keras-based code sample of a CNN with the MNIST dataset. The fifth chapter discusses RNNs (Recurrent Neural Networks), BPTT (Back Propagation Through Time), as well as LSTMs (Long Short Term Memory) and AEs (Auto Encoders). The sixth chapter introduces basic TensorFlow concepts, followedby tensorflowjs (i.e., TensorFlow in modern browsers), and some examples of Angular applications combined with deep learning.

This book is designed to provide the reader with basic Python3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. Features: Provides the reader withbasic Python3 programming concepts related to machine learning Includes separate appendices for regular expressions, Keras, and TensorFlow 2

As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, and data cleaning. The book includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regular expressions. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3. Provides a thorough introduction to data and data cleaning. Covers NumPy and Pandas. Introduces statistical concepts and data visualization (Matplotlib/Seaborn). Features an appendix on regular expressions. Includes companion files with source code and figures.

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learningclassifiers such as logistic regression, k-NN, decision trees, random forests,and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs,LSTMs, and auto encoders. Keras-based code samples are included to supplementthe theoretical discussion. In addition, this book contains appendices forKeras, TensorFlow 2, and Pandas.