A new progression fantasy epic featuring a detailed magic system, kingdom building, and plenty of action.A regression ritual gone wrong. A mage reborn in an ancient era. A world to rebuild. Kai of the Sorcerer's Tower was one of the last Mages left, barely surviving in a world contaminated with dead mana and ruled by creatures of the dark.To save his world and fulfill his master's last wish, he puts himself through a forbidden ritual, risking his soul being ripped into pieces to make his way into the past, to a golden era with abundant mana.But things go wrong. He overshoots and finds himself in an ancient time thousands of years earlier, within the body of a young lord named Arzan.All is not well, though. The young lord was murdered by mysterious forces before Kai took over, beasts are rampant, and even larger threats loom on the horizon.There's a lot to do for Kai to regain his former strength in this time of primitive technology. Then again, he might just be able to change that and makes things even better than they ever were.Don't miss the start of this Progression Fantasy Epic about a desperate mage sent into the past to rescue his apocalyptic world. Features a detailed magic system, kingdom building, three-dimensional characters, plenty of action, and everything you love about progression fantasy! Read more

"Flowers for Algernon" is a poignant science fiction novel by Daniel Keyes that explores the complexities of human intelligence, love, and the ethical implications of scientific experimentation. The story revolves around Charlie Gordon, a mentally challenged man in his thirties, who is chosen to undergo an experimental surgery aimed at increasing his intelligence. As the procedure is conducted, readers are introduced to Charlie's journey through progress and regression, told through a series of journal entries that reveal his evolving self-awareness and emotional depth.The title references Algernon, a laboratory mouse who undergoes the same procedure and serves as both a companion and a metaphor for Charlie's own struggles. As Charlie’s intelligence blossoms, he grapples with new relationships, the isolation it brings, and the painful awareness of his past and present. However, the narrative takes a heart-wrenching turn as the effects of the surgery begin to fade, prompting profound questions about the nature of intellect, the value of human connections, and the inevitability of change.Keyes’ profound exploration of human dignity, the importance of memory, and the quest for understanding makes "Flowers for Algernon" an enduring classic that resonates deeply with readers. It's a powerful reflection on what it means to be truly human and the fragility of our existence. Read more

Only Sonya remembers the end of the world.The apocalypse started slowly. People suddenly gained superhuman abilities. Then came the monster-filled gates. Humanity persevered over everything thrown their way – only to destroy themselves.Sent back to the apocalypse’s start, Sonya awakens a mythic ability with unlimited potential. She must find those who destroyed the world – and end them.To carry out her plans, she embraces two identities, one fighting in the light, the other ruling in the shadows, playing cat-and-mouse games with investigators, hired assassins, tycoons, and world leaders. As her power grows, so does the weight of her decisions—and the line between her identities blurs.The ends will justify the means. But only if she wins.Broker is a top serialized novel on Royal Road with over 6k followers and millions of views. The series features a protagonist with limitless potential in an apocalyptic setting with a superhero progression system.Buy a copy or read free with Kindle Unlimited today! Read more

Python Programming

Quick Python tour: Build a simple demo that includes data representation, object-oriented programming, object persistence, GUIs, and website basics
System programming: Explore system interface tools and techniques for command-line scripting, processing files and folders, running programs in parallel, and more
GUI programming: Learn to use Python’s tkinter widget library
Internet programming: Access client-side network protocols and email tools, use CGI scripts, and learn website implementation techniques
More ways to apply Python: Implement data structures, parse text-based information, interface with databases, and extend and embed Python

Machine Learning

How to download free datasets
What tools and machine learning libraries you need
Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
Preparing data for analysis, including k-fold Validation
Regression analysis to create trend lines
Clustering, including k-means and k-nearest Neighbors
The basics of Neural Networks
Bias/Variance to improve your machine learning model
Decision Trees to decode classification
How to build your first Machine Learning Model to predict house values using Python

Through a recent series of 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. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning Read more

A young man wanting to explore past-life regression discovers this is his first lifetime on Earth. All his other existences were on alien worlds and in other dimensions. As this unique case is explored further, he finds out that his association with extraterrestrials did not cease with these other lives. The interaction with UFOs and aliens has continued during all his present life, although the information was protectively hidden by his subconscious. This strange case reveals the motives behind present-day involvement between UFOs and humans and the hopes and expectations of our creators for their garden. We are the garden - the gardeners are extraterrestrials from other worlds. Read more

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."Everything you really need to know in Machine Learning in a hundred pages. Read more

La historia real de un psiquiatra, su joven paciente y la terapia de regresión que cambió sus vidas para siempre. Un punto de encuentro entre ciencia y metafísica. El doctor Brian Weiss, jefe de psiquiatría del hospital Mount Sinai de Miami, relata en éste, si primer libro, una asombrosa experiencia que cambió por completo su propia vida y su visión de la psicoterapia. Una de sus pacientes, Catherine, recordó bajo hipnosis varias de sus vidas pasadas y pudo encontrar en ellas el origen de muchos de los traumas que sufría. Catherine se curó, pero ocurrió algo todavía más importante: logró ponerse en contacto con los Maestros, espíritus superiores que habitan los estados entre dos vidas. Ellos le comunicaron importantes mensajes de sabiduría y de conocimiento. Este relato, profundamente conmovedor, punto de encuentro entre ciencia y metafísica, constituyó un extraordinario best seller y sigue siendo de obligada lectura en un mundo convulsionado, en especial para los que buscan un sentido espiritual.ENGLISH DESCRIPTIONThe true story about a psychiatrist, his young patient, and a regression therapy that changed their lives forever. It is a meeting point between science and metaphysics.   Dr. Brian Weiss, head of psychiatry at the Mount Sinai hospital in Miami, narrates in this his first book, the astounding events that change his life and his vision of what is psychotherapy. Catherine, one of his patients, while under hypnosis was able to find many of her past lives and there, found the origins of various of the traumas that she was currently going through.   Catherine was cured, but something even more important happened: she managed to get in touch with the Masters, higher spirits that live in between two lives; they passed down important messages of wisdom and knowledge to her. This deeply moving story and a meeting point between science and metaphysics, was an extraordinary best seller and continues to be a must-read in a troubled world, especially for those who seek spiritual guidance. Read more

Essential #1 best-selling reference for more than 20 years sold in almost every college bookstore in the country. The core statistics course is a critical and difficult class for many and develops a foundation that will support use of statistics in higher level courses in so many subject areas and majors. This six-page laminated guide is filled with more need-to-know facts per page than any book or website and will continue to support in future study. The equations, tables, and graphs that matter most accompany concisely written explanations in an outline format for easy access. This reference tool for students and professionals is proven to work selling millions since it's release. With the answers you need at your fingertips this durable tool will last your entire career and is an incredible value. 6 page laminated guide includes: Terminology Frequency Distribution Measures of Central Tendency Measures of Dispersion Graphing Techniques Probability Random Variables Continuous Random Variables Statistical Inference Biased & Unbiased Estimators Standard Error of the Mean Hypothesis Testing Central Limit Theorem Inference for Population Mean Using the Z-Statistic Inference for Population Mean using the t-Statistic Confidence Intervals Comparing Population Means Comparing Variances Analysis of Variance (ANOVA) Proportions Correlation Chi-Square Tests Hypothesis Test for Linear Correlation Regression Simple Linear Regression Significance of a Regression Model Linear Determination Residuals Read more

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnExplore frameworks, models, and techniques for machines to 'learn' from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering AnalysisImplementing a Multilayer Artificial Neural Network from Scratch(N.B. Please use the Look Inside option to see further chapters) 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

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

From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an accessible and fun guide to the essential tools of econometric researchApplied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu–themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful.The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse.Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect.Shows why econometrics is importantExplains econometric research through humorous and accessible discussionOutlines empirical methods central to modern econometric practiceWorks through interesting and relevant real-world examples Read more

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Read more

Make better decisions with this easy deconstructed guide to data analytics.Want to add data analytics to your skill stack? Having trouble finding where to start?Cell-by-cell, bit-by-bit, this audiobook teaches you the vocabulary, tools, and basic algorithms to think like a data scientist.Like putting together a complex Lego set, each section connects and adds individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.Who is this audiobook for? This audiobook is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this audiobook is for you.Practical approach. This audiobook takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the audiobook, you will have the practical knowledge to tackle real data problems in your organization or daily life.What you will learn:How to recognize the common data types every data scientist needs to masterWhere to store your data, including big dataNew trends in data analytics, including what is alternative data and why not many people know about itHow to explain the distinction between data mining, machine learning, and analytics to your colleaguesWhen and how to use regression analysis, classification, clustering, association analysis, and natural language processingHow to make better business decisions using data visualization and business intelligencePLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more

Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science. This book shows you how to get started. About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications. What's inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout About the reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions Read more

Now in its Seventh Edition, Neil J. Salkind’s bestselling Statistics for People Who (Think They) Hate Statistics with new co-author Bruce B. Frey teaches an often intimidating subject with a humorous, personable, and informative approach that reduces statistics anxiety. With instruction in SPSS®, the authors guide students through basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more.  The Seventh Edition includes new real-world examples, additional coverage on multiple regression and power and effect size, and a robust interactive eBook with video tutorials and animations of key concepts. In the end, students who (think they) hate statistics will understand how to explain the results of many statistical analyses and won’t be intimidated by basic statistical tasks. Read more

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.But rather than spend $30-$50 USD on a thick textbook, you may want to read this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations. Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.New Updated EditionThis new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.In This Step-By-Step Guide You Will Learn• How to download free datasets • What tools and machine learning libraries you need • Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data • Preparing data for analysis, including k-fold Validation • Regression analysis to create trend lines • k-Means Clustering to find new relationships • The basics of Neural Networks • Bias/Variance to improve your machine learning model • Decision Trees to decode classification, and • How to build your first Machine Learning Model to predict house values using PythonFrequently Asked QuestionsQ: Do I need programming experience to complete this e-book? A: This e-book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see some programming used in this book. Q: I have already purchased the Second Edition of Machine Learning for Absolute Beginners, should I purchase this Third Edition? A: As the same topics from the Second Edition are covered in the Third Edition, you may be better served reading a more advanced title on machine learning. If you have purchased a previous edition of this book and wish to get access to the free video tutorials, please email the author. Q: Does this book include everything I need to become a machine learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.Please feel welcome to join this introductory course by buying a copy, or sending a free sample to your chosen device. Read more

This book is written for high school and college students learning about probability for the first time. It will appeal to the reader who has a healthy level of enthusiasm for understanding how and why the various results of probability come about. All of the standard introductory topics in probability are covered: combinatorics, the rules of probability, Bayes’ theorem, expectation value, variance, probability density, common distributions, the law of large numbers, the central limit theorem, correlation, and regression. Calculus is not a prerequisite, although a few of the problems do involve calculus. These are marked clearly.The book features 150 worked-out problems in the form of examples in the text and solved problems at the end of each chapter. These problems, along with the discussions in the text, will be a valuable resource in any introductory probability course, either as the main text or as a helpful supplement. Read more

Advanced Statistics in Research: Reading, Understanding, and Writing Up Data Analysis Results is the simple, nontechnical introduction to the most complex multivariate statistics presented in empirical research articles.wwwStatsInResearch.com, is a companion website that provides free sample chapters, exercises, and PowerPoint slides for students and teachers. A free 600-item test bank is available to instructors. Advanced Statistics in Research does not show how to perform statistical procedures--it shows how to read, understand, and interpret them, as they are typically presented in journal articles and research reports. It demystifies the sophisticated statistics that stop most readers cold: multiple regression, logistic regression, discriminant analysis, ANOVA, ANCOVA, MANOVA, factor analysis, path analysis, structural equation modeling, meta-analysis--and more.Advanced Statistics in Research assumes that you have never had a course in statistics. It begins at the beginning, with research design, central tendency, variability, z scores, and the normal curve. You will learn (or re-learn) the big-three results that are common to most procedures: statistical significance, confidence intervals, and effect size. Step-by-step, each chapter gently builds on earlier concepts. Matrix algebra is avoided, and complex topics are explained using simple, easy-to-understand examples.Need help writing up your results? Advanced Statistics in Research shows how data-analysis results can be summarized in text, tables, and figures according to APA format. You will see how to present the basics (e.g., means and standard deviations) as well as the advanced (e.g., factor patterns, post-hoc tests, path models, and more). Advanced Statistics in Research is appropriate as a textbook for graduate students and upper-level undergraduates (see supplementary materials at StatsInResearch.com). It also serves as a handy shelf reference for investigators and all consumers of research. Read more

Data is collected constantly: how far we travel, who we interact with online and where we spend our money. Every bit of data has a story to tell but isolated, these morsels of information lie dormant and useless, like unattached Lego blocks. Written by the author of Amazon Best Seller Machine Learning for Absolute Beginners, this book guides you through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, visual examples, historical origins, and plain English explanations. As a resource for beginners, this book won't teach you how to beat the market or predict the next U.S. election but ensures a concise and simple-to-understand supplement to a standard textbook.This includes an introduction to important techniques used to infer predictions from data, such as hypothesis testing, linear regression analysis, confidence intervals, probability theory, and data distribution. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.Full Overview of Book Themes• The Historical Development of Statistics• Data Sampling • Central Tendency Measures • Measures Of Spread • Measures Of Position• Designing Hypothesis Tests• Probability and Bayes Theory• Regression Analysis• Clustering AnalysisAs the launchpad to quantitative research, business optimization or a promising career in data science, it's never been a better time to brush up on statistics or learn these concepts for the very first time. Read more

Offering both comfort to the fearful and confirmation to the curious, Between Death and Life – Conversations with a Spirit examines different levels of existence in the spirit realms through hundreds of real people's past life testimonies as revealed to widely published and internationally acclaimed past-life regressionist and hypnotherapist Dolores Cannon.- What happens at the point of death?- Where do we go afterwards?- Does one's personality survive after death?- How are the good and the bad experiences of life accounted for?- What is the purpose of life?These are questions everybody asks. And no one is better qualified to provide reasonable answers than Dolores Cannon. During forty years of detailed research, this widely experienced and well-respected American past-life regression therapist has accumulated a mass of credible information about the death experience and what lies beyond.While reliving their dying experiences, hundreds of subjects reported the same memories. The similarity and sincerity of their recollections are too convincing to be ignored.This eye-opening book explores the world beyond ours, giving us an insight into the death experience and reincarnation, guides and guardian angels, ghosts and walk-ins. It examines different levels of existence in the spirit realms; the 'Healing Chambers' for the damaged; the schools where you integrate lessons learned on Earth and where you discover the laws of the Universe; how you plan your next incarnation, the lessons to be learned and future karmic relationships before birth.Between Death and Life: Table of Contents- The Death Experience- The Greeters- A Near Death Experience- The Schools- The Grand Tour- The Different Levels or Planes of Existence- So-Called 'Bad' Lives- Guides- God and Jesus- Satan, Possession and Demons- Ghosts and Poltergeists- Planning and Preparation- The General Council- Imprinting- Walk-ins- The Return Trip 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

The author of Journey of Souls and Destiny of Souls presents case studies that highlight the profound impact of spiritual regression on people's lives.These fascinating true accounts from around the world are handpicked and presented by Life Between Lives hypnotherapists certified by the Newton Institute and edited by Dr. Newton. After recalling memories of their afterlife, the people in these studies embarked on life-changing spiritual journeys—reuniting with soul mates and spirit guides, and discovering the ramifications of life and  body choices, love relationships, and dreams by communing with their immortal souls. As gems of self-knowledge are revealed, dramatic epiphanies result, enabling these ordinary people to understand adversity in their lives, find emotional healing, realize their true purpose, and forever enrich their lives with new meaning. Read more

Presents an accessible approach to the cost estimation tools, concepts, and techniques needed to support analytical and cost decisionsWith an easy-to-understand approach, Cost Estimation: Methods and Tools provides comprehensive coverage of the quantitative techniques needed by professional cost estimators and for those wanting to learn about this vibrant career field. Featuring the underlying mathematical and analytical principles of cost estimation, the book focuses on the tools and methods used to predict the research and development, production, and operating and support costs for successful cost estimation in industrial, business, and manufacturing processes.The book begins with a detailed historical perspective and key terms of the cost estimating field in order to develop the necessary background prior to implementing the presented quantitative methods. The book proceeds to fundamental cost estimation methods utilized in the field of cost estimation, including working with inflation indices, regression analysis, learning curves, analogies, cost factors, and wrap rates.With a step-by-step introduction to the practicality of cost estimation and the available resources for obtaining relevant data, Cost Estimation: Methods and Tools also features: Various cost estimating tools, concepts, and techniques needed to support business decisionsMultiple questions at the end of each chapter to help listeners obtain a deeper understanding of the discussed methods and techniquesAn overview of the software used in cost estimation, as well as an introduction to the application of risk and uncertainty analysis A foreword from Dr. Douglas A. Brook, a professor in the Graduate School of Business and Public Policy at the Naval Postgraduate School, who spent many years working in the Department of Defense acquisition environmentCost Estimation: Methods and Tools is an excellent reference for academics and practitioners in decision science, operations research, operations management, business, and systems and industrial engineering, as well as a useful guide in support of professional cost estimation training and certification courses for practitioners. The book is also appropriate for graduate-level courses in operations research, operations management, engineering economics, and manufacturing and/or production processes.PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more

The essential futures market reference guideA Complete Guide to the Futures Market is the comprehensive resource for futures traders and analysts. Spanning everything from technical analysis, trading systems, and fundamental analysis to options, spreads, and practical trading principles, A Complete Guide is required reading for any trader or investor who wants to successfully navigate the futures market.Clear, concise, and to the point, this fully revised and updated second edition provides a solid foundation in futures market basics, details key analysis and forecasting techniques, explores advanced trading concepts, and illustrates the practical application of these ideas with hundreds of market examples. A Complete Guide to the Futures Market:Details different trading and analytical approaches, including chart analysis, technical indicators and trading systems, regression analysis, and fundamental market models.Separates misleading market myths from reality.Gives step-by-step instruction for developing and testing original trading ideas and systems.Illustrates a wide range of option strategies, and explains the trading implications of each.Details a wealth of practical trading guidelines and market insights from a recognized trading authority.Trading futures without a firm grasp of this market’s realities and nuances is a recipe for losing money. A Complete Guide to the Futures Market offers serious traders and investors the tools to keep themselves on the right side of the ledger. Read more

From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an irreverent guide to the essentials of econometricsThe core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions―regression-discontinuity designs and quantile regression―as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.An irreverent review of econometric essentialsA focus on tools that applied researchers use mostChapters on regression-discontinuity designs, quantile regression, and standard errorsMany empirical examplesA clear and concise resource with wide applications Read more

📘 Math Behind the Machines: AI Explained for Safer, Smarter, and More Responsible Decision MakingArtificial intelligence shouldn’t be a mystery. It should be a tool for better choices.From hiring decisions and medical diagnoses to marketing, finance, and public policy, AI systems are influencing the most critical choices in business, government, and everyday life. But behind the sleek dashboards and smart predictions lies something surprisingly familiar: math—specifically, the powerful tools of statistics.In Math Behind the Machines, you’ll discover how foundational concepts like regression, probability, and statistical inference power today’s AI systems. Whether you’re a leader, advisor, domain expert, or curious professional, this book will help you make more confident, informed, and ethical decisions about the technologies shaping our world.🔍 Inside, you’ll learn how to:Demystify machine learning with plain-language explanationsUnderstand the risks, assumptions, and trade-offs built into AI modelsIdentify early signs of bias or data drift before they lead to costly mistakesAsk the right questions when evaluating AI tools, vendors, or proposalsAlign your organization’s use of AI with values like fairness, transparency, and safetyUnderstand why AI data governance matters—and how to spot risks in how data is collected, labeled, and usedGrasp what AI model governance really means, and how to ensure AI systems stay reliable, fair, and accountable over time🧠 Who This Book Is For:Business leaders, board members, and policymakers guiding AI strategyProfessionals in healthcare, finance, marketing, law, and operations working with AI toolsConsultants, educators, and innovators building or advising on responsible technologyCurious learners and non-technical professionals who want to understand AI—without diving into codeIf you care about making smarter, safer, and more responsible decisions in the age of AI, this book is your essential guide.Because when you understand the math, you understand the machine. Read more

Continue your statistics journey with this all-encompassing reference Completed Statistics through standard deviations, confidence intervals, and hypothesis testing? Then you’re ready for the next step: Statistics II. And there’s no better way to tackle this challenging subject than with Statistics II For Dummies! Get a brief overview of Statistics I in case you need to brush up on earlier topics, and then dive into a full explanation of all Statistic II concepts, including multiple regression, analysis of variance (ANOVA), Chi-square tests, nonparametric procedures, and analyzing large data sets. By the end of the book, you’ll know how to use all the statistics tools together to create a great story about your data. For each Statistics II technique in the book, you get an overview of when and why it’s used, how to know when you need it, step-by-step directions on how to do it, and tips and tricks for working through the solution. You also find:  What makes each technique distinct and what the results say How to apply techniques in real life An interpretation of the computer output for data analysis purposes Instructions for using Minitab to work through many of the calculations Practice with a lot of examples With Statistics II For Dummies, you will find even more techniques to analyze a set of data. Get a head start on your Statistics II class, or use this in conjunction with your textbook to help you thrive in statistics!  Read more

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming languageMachine learning―a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions―allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reductionCovers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clusteringDescribes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniquesExplains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoostPractical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field. Read more