Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandasKey FeaturesDiscover new and updated content on NLP transformers, PyTorch, and computer vision modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts.Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learnFollow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid overfitting using regularization, feature selection, and moreWho this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.Table of ContentsGetting Started with Machine Learning and PythonBuilding a Movie Recommendation EnginePredicting Online Ad Click-Through with Tree-Based AlgorithmsPredicting Online Ad Click-Through with Logistic RegressionPredicting Stock Prices with Regression AlgorithmsPredicting Stock Prices with Artificial Neural NetworksMining the 20 Newsgroups Dataset with Text Analysis TechniquesDiscovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic ModelingRecognizing Faces with Support Vector MachineMachine Learning Best PracticesCategorizing Images of Clothing with Convolutional Neural NetworksMaking Predictions with Sequences Using Recurrent Neural NetworksAdvancing Language Understanding and Generation with Transformer ModelsBuilding An Image Search Engine Using Multimodal ModelsMaking Decisions in Complex Environments with Reinforcement Learning Read more

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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data Read more

There are many textbooks devoted to the theory behind Monte Carlo methods. More often than not, these are heavy on theory and light on example. Rarely do they include the examples in their entirety, mostly presenting the final results in summary form. The aim of this text is to be light on theory and heavy on example. Each example is included in its entirety: input, output, and source code or spreadsheet. If you work through all the examples, you should be able to simulate whatever process is needed. Read more

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Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining. Read more

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility. Read more

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includesExplore R, RStudio, and R packagesUse R for math: variable types, vectors, calling functions, and moreExploit data structures, including data.frames, matrices, and listsRead many different types of dataCreate attractive, intuitive statistical graphicsWrite user-defined functionsControl program flow with if, ifelse, and complex checksImprove program efficiency with group manipulationsCombine and reshape multiple datasetsManipulate strings using R’s facilities and regular expressionsCreate normal, binomial, and Poisson probability distributionsBuild linear, generalized linear, and nonlinear modelsProgram basic statistics: mean, standard deviation, and t-testsTrain machine learning modelsAssess the quality of models and variable selectionPrevent overfitting and perform variable selection, using the Elastic Net and Bayesian methodsAnalyze univariate and multivariate time series dataGroup data via K-means and hierarchical clusteringPrepare reports, slideshows, and web pages with knitrDisplay interactive data with RMarkdown and htmlwidgetsImplement dashboards with ShinyBuild reusable R packages with devtools and RcppRegister your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. Read more

The SAS® Certified Specialist Prep Guide: Base Programming Using SAS® 9.4 prepares you to take the new SAS 9.4 Base Programming -- Performance-Based Exam. This is the official guide by the SAS Global Certification Program. This prep guide is for both new and experienced SAS users, and it covers all the objectives that are tested on the exam. New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers for the chapter quizzes and solutions for the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS® glossary and a list of practice data sets. Major topics include importing data, creating and modifying SAS data sets, and identifying and correcting both data syntax and programming logic errors. All exam topics are covered in these chapters: Setting Up Practice Data Basic Concepts Accessing Your Data Creating SAS Data Sets Identifying and Correcting SAS Language Errors Creating Reports Understanding DATA Step Processing BY-Group Processing Creating and Managing Variables Combining SAS Data Sets Processing Data with DO Loops SAS Formats and Informats SAS Date, Time, and Datetime Values Using Functions to Manipulate Data Producing Descriptive Statistics Creating Output Practice Programming Scenarios (Workbook) Read more

Ready to Transform Your Excel Skills?🌟 EXCLUSIVE BONUSES AWAIT: 7 Hours of Video Tutorials + 5 Ebooks + 200 Templates! 🌟"Excel 2025" is your gateway to advanced Excel capabilities. Dive into transformative content brimming with key functions, formulas, shortcuts, and beyond. Prepare for an unparalleled Excel adventure, bolstered by phenomenal bonuses:✅ BONUS 1: Supercharge Your Work with 200+ Ready-to-Use Templates✅ BONUS 2: Unlock Formula Mastery with the "500 Excel Formulas" Ebook✅ BONUS 3: Streamline Efficiency with "Productivity Hacks for Office Workers" Ebook✅ BONUS 4: Fortify Professional Communications with "The Art of Communication in the Workplace" Ebook✅ BONUS 5: Navigate Remote Work with Ease through "Remote Work: Tips and Best Practices" Ebook✅ BONUS 6: Elevate Your Data Skills with "Automation and Scripting in Excel: Integrating Data Analysis and AI" Ebook📚 PLUS A SUPER BONUS: Begin your Excel educational journey with three tailor-made Video Courses for all expertise levels:- Beginner Course: Solidify your Excel basics.- Intermediate Course: Explore Excel's intricate features.- Advanced Course: Command Excel's full spectrum of functionalities.Excel Skills: Your Professional EdgeExcel prowess is a staple across myriad industries. Given that around 80% of roles necessitate Excel proficiency, sharpening your skills can boost your employability by as much as 20%. "Excel 2025" aligns seamlessly with your aspirations, steering you towards career progression and unmatched opportunities.No matter your current proficiency - a newbie, a veteran, or an Excel enthusiast - "Excel 2025" is crafted just for you.Distinctive Features of This Book:📘 Holistic Learning Journey: Navigate through 13 exhaustive chapters, each sculpted to inch you closer to Excel mastery.🌍 Global Relevance: Whether it's a bustling business epicenter or a scholastic hub, this guide ensures your Excel skills shine universally.💼 Career Catalyst: Eyeing prestigious roles? Chapter 13 illuminates the path to coveted positions that prize Excel adeptness.🎯 Practical Mastery: Beyond mere theory; delve into real-world applications, tips, and datasets for hands-on expertise.🌠 Rise in your professional path with unparalleled Excel skills. Don't be just another face in the crowd. Submerge into this all-inclusive guide, refine your expertise, and be the Excel maestro everyone seeks. The future of Excel is here; seize your copy of "Excel 2025" today! 🌠 Read more

The quantum computing market is predicted to grow by nearly $1.3 billion over the next five years. Why? Given their quantum mechanical nature, quantum computers are expected to solve difficult problems in chemistry, optimization, finance, and machine learning that classical computers find impossible to unravel.This pocket guide provides software developers with a quick reference to Qiskit, an open source SDK for working with quantum computers. Packed with helpful programming examples, tables, figures, and lists, this handy book helps you find the information you need to develop and debug quantum programs.Whether you're focused on business, engineering, or scientific applications, you'll learn how to choose and apply appropriate Qiskit classes, methods, and functions.Learn how to create quantum circuits, define quantum gates, and leverage the transpilerExplore modules for implementing quantum information concepts and quantum algorithmsSurvey features of Qiskit that abstract and facilitate working with various quantum computers and simulatorsLearn how to use the latest version of the Open Quantum Assembly Language Read more

The Second Edition of Advanced Issues in Partial Least Squares Structural Equation Modeling offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of A Primer on Partial Least Squares Structural Equation Modeling, Third Edition. Even in this advanced guide, the authors have limited the emphasis on equations, formulas, and Greek symbols, and instead rely on detailed explanations of the fundamentals of PLS-SEM and provide general guidelines for understanding and evaluating the results of applying the method. A single study on corporate reputation features as an example throughout the book, along with a single software package (SmartPLS 4.0) to provide a seamless learning experience. The approach of this book is based on the authors’ many years of conducting research and teaching methodology courses, including developing the SmartPLS software. The preparation of the book, especially this new edition, is based on the authors’ desire to communicate the PLS-SEM method to a much broader audience from management and marketing to engineering, geography, medicine, political and environmental sciences, psychology, and beyond. The Second Edition includes a new chapter on the necessary condition analysis (NCA) and covers the most recent developments in PLS-SEM, with detailed guidelines for estimating and validating higher-order constructs and nonlinear effects as well as more insights on multigroup and latent class analyses using FIMIX-PLS and PLS-POS. The book is aimed at researchers and practitioners who seek to gain comprehensive knowledge of more advanced PLS-SEM methods. Read more

Quantum computers are poised to kick-start a new computing revolution—and you can join in right away. If you’re in software engineering, computer graphics, data science, or just an intrigued computerphile, this book provides a hands-on programmer’s guide to understanding quantum computing. Rather than labor through math and theory, you’ll work directly with examples that demonstrate this technology’s unique capabilities.Quantum computing specialists Eric Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia show you how to build the skills, tools, and intuition required to write quantum programs at the center of applications. You’ll understand what quantum computers can do and learn how to identify the types of problems they can solve.This book includes three multichapter sections:Programming for a QPU—Explore core concepts for programming quantum processing units, including how to describe and manipulate qubits and how to perform quantum teleportation.QPU Primitives—Learn algorithmic primitives and techniques, including amplitude amplification, the Quantum Fourier Transform, and phase estimation.QPU Applications—Investigate how QPU primitives are used to build existing applications, including quantum search techniques and Shor’s factoring algorithm. Read more

Franz Buscha's book, Graphs Everyone Should Know and How to Create Them in Stata, is written for anyone who uses Stata to make graphs. Beginners will find a complete collection of tools for effectively visualizing their data and results. Experienced Stata users are certain to learn some new tricks as well. The chapters of the book are organized into four main sections: graphs for univariate data, graphs for bivariate data, graphs for multivariate data, and special graphs. Each chapter introduces a type of graph, explains when and why it is useful for visualizing a particular kind of data, demonstrates how to create that graph using Stata, and shows a few variations. The special graph section covers topics such as how to create maps, plot equations, create animated graphs, and create other specialty graphs.Readers will find it easy to learn to make graphs by example. Buscha demonstrates most graphs using datasets that are installed with your copy of Stata, so it is straightforward to follow along. He also clearly pairs each graph with the command used to create it in a box just above the graph. If you find a graph that you wish to create with your own data, you can take the command from the box and replace the variable names in the example with your own variable names. Buscha's book has two unique features that distinguish it from other books about Stata graphs. First, the book's goal is to clearly demonstrate how to effectively visualize different kinds of data and results from models using only necessary features. It focuses on important options but does not discuss all the options available for customizing each graph. Second, the book introduces many community-contributed graph commands that are freely available and can be downloaded from the internet. Readers may be unaware of these commands before finding them in this book, and they can learn how to use them quickly rather than spend time trying to write custom code for themselves. Graphs Everyone Should Know and How to Create Them in Stata is a reference you will use again and again as you visualize different types of data. You will quickly find the graphs that are applicable to your data and the Stata commands necessary to create them. Read more

This comprehensive guide takes you on a structured journey through the world of data analysis, using powerful tools like Python, R, and SQL. Covering everything from foundational statistical concepts to advanced techniques, the book explores essential topics such as data manipulation, exploratory data analysis (EDA), and predictive modeling with regression and time series analysis.It also delves into cutting-edge topics like social media data analysis, machine learning, and interactive visualizations. Dedicated chapters on ethics in data analysis and future trends, including artificial intelligence and real-time analytics, provide a forward-looking perspective.Packed with case studies and practical examples, this book is perfect for beginners and professionals looking to enhance their data analysis skills and make informed, data-driven decisions in an increasingly data-centric world. Read more

"CYBERSECURITY DICTIONARY FOR EVERYONE": GO BEYOND WHAT GOOGLE AND AI CAN OFFER!Every 39 seconds, a cyberattack strikes somewhere in the world. In 2023 alone, cybercrime is projected to cost the global economy a staggering $8 trillion – more than the GDP of most countries. The internet is a battlefield, and you're in the crosshairs.Your bank account, medical records, personal photos, even your child's identity—all are vulnerable to cybercriminals who lurk in the digital shadows. Are you prepared to defend yourself?"Cybersecurity Dictionary for Everyone"is your essential weapon in this digital war zone. It's more than just a dictionary; it's a survival guide, designed to empower you with the knowledge you need to protect everything you hold dear.Inside this arsenal of knowledge, you'll discover:1250 Essential Cybersecurity Terms: A comprehensive collection of the most critical terms you need to know, from "phishing" to "ransomware" to "zero-day exploit."Crystal-Clear Definitions: No more tech jargon. Every term is explained in plain, simple English that anyone can understand, regardless of their technical background.Real-World Examples: Each definition is brought to life with real-world examples, illustrating how these concepts play out in your everyday life and the headlines you read.Related Terms: Connections between concepts are highlighted, helping you see the bigger picture and understand the interconnectedness of the cybersecurity landscape.This book is your ultimate defence against the ever-evolving threat landscape. It goes beyond simple definitions, providing clear explanations and real-world examples to help you:Stay informed: Understand the latest cyber threats and vulnerabilities.Protect yourself: Secure your personal information and devices from hackers.Make informed decisions: Choose the right security solutions for your needs.Secure your business: Safeguard your company's data, systems, and reputation.Empower your students: Educate the next generation of cybersecurity professionals.Whether you're a concerned parent, a business owner, a student pursuing a cybersecurity certificate, or simply someone who realizes the importance of digital security, this dictionary is your indispensable companion.Don't wait until it's too late. Arm yourself with knowledge and take control of your digital destiny. The "Cybersecurity Dictionary for Everyone" is your first step towards a safer, more secure digital future. Read more

Random numbers have a lot of useful and fun properties. Not the least of which is that if you run into a problem that is too hard for a closed form solution, simulating it with random numbers is almost always easier. Excel has a fairly easy to use pseudo-random number generator. Coupled with Excel’s other good functions, it is very useful for generating a moderate quantity of random numbers for small to medium simulations. This book walks you through how to generate random numbers in Excel, and some variations you can use them for such as sorting, or making a normal distribution. Read more

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.What You'll LearnWork with vectors and matrices using NumPyReview Symbolic computing with SymPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyUnderstand statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.  Read more

“Roulette, The Beginner’s Guide”, provides an excellent well illustrated introduction to this signature casino game. It covers with clarity, the basics of roulette, the bets, the odds, and how to play roulette in a casino with respect of game rules and etiquette. Full and clear color illustrations together with detailed explanations will guide the reader effortlessly from a position of no knowledge to a capability and confidence to practically play the game. No roulette beginner should be without this concise extensively illustrated reference. Read more

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer’s memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R’s package system and debugging toolsPractice and apply R programming concepts as you learn them Read more