Coding Interview Patterns - Nail Your Next Coding InterviewCoding interviews are tough, and they're only getting tougher, typically demanding months of preparation. What we all want is a way to master algorithms and data structures without having to spend countless hours sifting through endless, unfocussed resources. Introducing "Coding Interview Patterns," your complete guide to mastering the key patterns needed to ace your next coding interview.What’s inside?An insider’s perspective on what interviewers are truly looking for and why.101 real coding interview problems with detailed solutions.Intuitive explanations that guide you through each problem as if you were solving it in a live interview.1000+ diagrams to illustrate key concepts and patterns.Table of ContentsChapter 1: Two PointersChapter 2: Hash Maps and SetsChapter 3: Linked ListsChapter 4: Fast and Slow PointersChapter 5: Sliding WindowsChapter 6: Binary SearchChapter 7: StacksChapter 8: HeapsChapter 9: IntervalsChapter 10: Prefix SumsChapter 11: TreesChapter 12: TriesChapter 13: GraphsChapter 14: BacktrackingChapter 15: Dynamic ProgrammingChapter 16: GreedyChapter 17: Sort and SearchChapter 18: Bit ManipulationChapter 19: Math and Geometry 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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. Read more

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto Read more

🎁8 Exclusive Bonuses Included👇Discover the Full Power of Excel in 48 Hours: From Beginner to Expert!Are you ready to stop feeling overwhelmed by Excel and start mastering it effortlessly? With this comprehensive and practical guide, you'll unlock Excel's most valuable features in record time, even if you're starting from scratch. 💡📊Our guide allows you to use never-before-seen functions to cut your work time in half. If you use Excel for 4 hours a day, imagine gaining 2 extra hours daily—fantastic, right?Here’s what this book offers:Designed for everyone—whether you're a student, entrepreneur, or seasoned professional—this book breaks down complex Excel concepts into simple, actionable steps. You won’t just learn how to create spreadsheets, but also how to transform raw data into meaningful insights that boost productivity and decision-making. 📈✅What You’ll Learn:• Streamline Your Workflow: Automate repetitive tasks with advanced formulas and macros. ⚙️💻• Simplify Data Analysis: Organize, filter, and visualize data with powerful tools like Pivot Tables and dynamic charts. 📊• Boost Your Productivity: Discover shortcuts and tips that will save you hours of work. ⏳🚀• Real-World Applications: Use Excel for budgeting, financial analysis, project management, and much more. 💼💡🎁 8 Exclusive Bonuses:• 13+ Hours of Step-by-Step Video Tutorials: These will guide you through everything from basic functions to advanced automation techniques. 🎥📚• 500+ Templates, Illustrations, Practical Exercises, and Quick Formulas: Instantly apply what you’ve learned with pre-built templates for various business and personal needs, or use exercises to quickly grasp new concepts. 📁🛠️• Excel Formulas Cheat Sheet: A quick-reference guide featuring essential Excel formulas to solve any data-related challenge in no time. 📄📊• How to Use Excel with Artificial Intelligence: Unlock the future of Excel by integrating AI tools for smarter data analysis and automation. 🤖📈• Keyboard Shortcut Guide for Excel, Office, and PC: A complete set of shortcuts to enhance your efficiency across Excel and the entire Microsoft Office suite, saving you hours of work. ⌨️🚀• Quick Formulas Guide: A detailed eBook covering essential Excel formulas to simplify complex calculations and increase your efficiency. 📘📈• Mastering Pivot Tables and Data Visualizations: Learn how to create impactful Pivot Tables and interactive charts with our exclusive bonus guide, based on advanced techniques for professional data visualization. 📊📝• Powerful Excel Tips & Formula Tricks: Get quick reference sheets with essential formulas and shortcuts to maximize your productivity in Excel, all in one handy resource. 📝💡With easy-to-follow lessons and practical exercises, this guide ensures you gain the skills to confidently navigate Excel and excel in your career. Whether you need to manage large datasets or create professional reports, everything you need is right here. 💪📊Start your Excel journey today: Empower yourself with the tools to succeed in today’s data-driven world.Click "Buy Now" and take control of your productivity! 🛒✨ 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

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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Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow Read more

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn:–The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops–Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R–How to access R’s thousands of functions, libraries, and data sets–How to draw valid and useful conclusions from your data–How to create publication-quality graphics of your resultsCombining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis. Read more

Unlock the Power of Bayesian Statistics with RThis comprehensive guide is designed for anyone who wants to delve into the practical application of Bayesian methods for modeling sports data. You will be exposed to a concise yet practical sequence of statistical concepts that get you on the path to sports modeling in R as quickly as possible – armed with just enough knowledge of Bayesian methods to make you moderately dangerous. Along the way, you'll build on what you learn in each chapter, starting with the basics of Bayesian reasoning and probability, all the way through to more complex topics like Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC). Each chapter is enriched with practical sports examples and fully downloadable R code, making it a hands-on and interactive experience.Culminating with fully coded R examples of working Bayesian models for a number of sports including the NHL, NBA, NFL & MLB, Mack's combination of downloadable code and clear explanations will allow readers with different levels of familiarity with statistics to level-up their sports modeling skills.Whether you are a seasoned sports bettor looking to enhance your Bayesian modeling skills or an R novice eager to improve your abilities, 'Bayesian Sports Models in R' has something for everyone. This book not only teaches you the theory behind Bayesian methods but also demonstrates how to apply these techniques to real-world sports scenarios. From predicting game outcomes to analyzing player performance, you'll learn how to leverage Bayesian models in the search for a competitive edge. Plus, you can easily implement and customize these models to suit your specific needs, making them a valuable resource for anyone serious about sports modeling through simulation.Bayesian methods are the gold standard for modeling events with large amounts of dynamic uncertainty.You’re about to find out why. Read more

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey FeaturesConduct Bayesian data analysis with step-by-step guidanceGain insight into a modern, practical, and computational approach to Bayesian statistical modelingEnhance your learning with best practices through sample problems and practice exercisesPurchase of the print or Kindle book includes a free PDF eBook.Book DescriptionThe third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What you will learnBuild probabilistic models using PyMC and BambiAnalyze and interpret probabilistic models with ArviZAcquire the skills to sanity-check models and modify them if necessaryBuild better models with prior and posterior predictive checksLearn the advantages and caveats of hierarchical modelsCompare models and choose between alternative onesInterpret results and apply your knowledge to real-world problemsExplore common models from a unified probabilistic perspectiveApply the Bayesian framework's flexibility for probabilistic thinkingWho this book is forIf you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.Table of ContentsThinking ProbabilisticallyProgramming ProbabilisticallyHierarchical ModelsModeling with LinesComparing ModelsModeling with BambiMixture ModelsGaussian ProcessesBayesian Additive Regression TreesInference EnginesWhere to Go Next Read more

AMS packages that replaced AMS-Ib-TEX, version 1.1, but the book still had an AMS-Ib-TEX-centric view. This third edition is about Ib-TEX. Where necessary, I recommend that you use packages to extend Ib-TEX's capabilities. For typesetting mathematics, I strongly recommend that you use the AMS packages. AMS packages, version 2.0 The American Mathematical Society released version 2.0 ofthe AMS packages in 1999. This third edition covers the changes made in this release. Books The first and second editions of this book dealt primarily with the tasks involved in writing artides. In Part V, the third edition addresses the issues that arise when creating longer documents. In addition to chapters on BIBTEX and Makelndex, I have added a new chapter on writing books. Appendix F illustrates the impor­ tance of choosing a well-designed book document dass. Other changes B\1EX Ib-TEX2 has been remarkably stable since its release in 1996, becorning c the standard Ib-TEX (see Section C.1.2). Changeshave been rninor except for ad­ vances in using Ib-TEX with non-English languages (see the new Appendix E) and the widespread use of the PostScript CM and AMS fonts (see the new Section D.l). Reorganization and additions Due to the new emphasis on writing books, a number of sections and subsections have moved from Chapter 2 and Chapters 6-8 to the new Chapter 12. Read more

This bookis a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis.With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. Read more

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems Read more

This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.The color images and text in this book have been converted to grayscale. Read more

An authoritative introduction to the latest comparative methods in evolutionary biologyPhylogenetic comparative methods are a suite of statistical approaches that enable biologists to analyze and better understand the evolutionary tree of life, and shed vital new light on patterns of divergence and common ancestry among all species on Earth. This textbook shows how to carry out phylogenetic comparative analyses in the R statistical computing environment. Liam Revell and Luke Harmon provide an incisive conceptual overview of each method along with worked examples using real data and challenge problems that encourage students to learn by doing. By working through this book, students will gain a solid foundation in these methods and develop the skills they need to interpret patterns in the tree of life.Covers every major method of modern phylogenetic comparative analysis in RExplains the basics of R and discusses topics such as trait evolution, diversification, trait-dependent diversification, biogeography, and visualizationFeatures a wealth of exercises and challenge problemsServes as an invaluable resource for students and researchers, with applications in ecology, evolution, anthropology, disease transmission, conservation biology, and a host of other areasWritten by two of today’s leading developers of phylogenetic comparative methods Read more

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangleâ??transform your datasets into a form convenient for analysis Programâ??learn powerful R tools for solving data problems with greater clarity and ease Exploreâ??examine your data, generate hypotheses, and quickly test them Modelâ??provide a low-dimensional summary that captures true "signals" in your dataset Communicateâ??learn R Markdown for integrating prose, code, and results Read more

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Unlock Your Engineering Potential with MATLAB!Are you ready to revolutionize your approach to engineering problem-solving? This comprehensive textbook is your ultimate guide to mastering MATLAB, meticulously designed to transform complex mathematical theories into practical, hands-on code that works in the real world.Complete MATLAB Code at Your Fingertips: Every chapter is packed with thoroughly commented MATLAB scripts—from performing real and complex arithmetic to tackling intricate topics like singular value decomposition and Laplace transforms. Imagine following detailed instructions on the Gram-Schmidt process and then immediately applying robust numerical methods to optimize engineering designs!Concepts That Build Your Expertise:– Dive deep into core topics such as matrix decomposition, numerical integration, and ODE solvers.– Explore advanced methods like Runge-Kutta techniques and gradient descent optimization with clear, step-by-step examples.– Gain invaluable insights into applied engineering math, including Fourier transforms and Z-transforms, that give you an edge in both academic and professional settings.Practical and Intuitive Explanations: Whether you’re a student eager to conquer your coursework or a professional aiming to polish your skills, this guide demystifies complex theories and presents them in an engaging, digestible format. Every concept is reinforced with hands-on exercises and real-world applications that ensure you understand not just the "how" but also the "why."Strengthen your toolkit with a resource that combines rigorous academic insight with practical MATLAB implementation. Empower your future with every page as you transform theory into practice! Read more

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