| Brief | Book | |
|---|---|---|
| Designed to provide Business students and professionals with a pragmatic introduction to R, RStudio, Git, GitHub, and GitKraken in the context of the Business Data Life Cycle.
R is a powerful free open source statistical computing software that has emerged as the leading programming language for statistics and data analysis. It is well established in both academia and corporations for robustness, reliability and accuracy, but is still relatively new to the Caribbean region. R allows the business to combine statistics with computer science in order to extract new insights and new knowledge from the vast amount of data generated from the digital age. Based on real cases, the book allows you will sit at the computer and learn how to load, merge, clean, reshape, summarize, troubleshoot and transform data into mathematical functions, and produce ‘markdown’ reports using R. Eventually, the book positions you to immediately increase their usefulness to the organization, as well as to move on to more advanced courses such as Data Science Using R. |
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Saturday, August 22, 2020
Introduction to R for Business
On the path to AI... foundations of the machine learning age
| Brief | Book | |
|---|---|---|
| This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age―prediction based on datasets.
On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, and biased data. |
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SQL QuickStart Guide...
| Brief | Book | |
|---|---|---|
| The ubiquity of big data means that now more than ever there is a burning need to warehouse, access, and understand the contents of massive databases quickly and efficiently.
That’s where SQL comes in. SQL is the workhorse programming language that forms the backbone of modern data management and interpretation. Any database management professional will tell you that despite trendy data management languages that come and go, SQL remains the most widely used and most reliable to date, with no signs of stopping. In this comprehensive guide, experienced mentor and SQL expert Walter Shields draws on his considerable knowledge to make the topic of relational database management accessible, easy to understand, and highly actionable. This book is ideal for those seeking to increase their job prospects and enhance their careers, for developers looking to expand their programming capabilities, or for anyone who wants to take advantage of our inevitably data-driven future—even with no prior coding experience! |
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Linear Algebra and Learning from Data
| Book | Brief | |
|---|---|---|
| This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text. |
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Python for Finance... Mastering Data-Driven Finance
| Book | Brief | |
|---|---|---|
| The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics.
Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full fledged framework for Monte Carlo simulation based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
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Neural Networks and Learning Machines...
| Brief | Book | |
|---|---|---|
| Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently. |
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Artificial Intelligence and Machine Learning for Digital Pathology...
| Brief | Book | |
|---|---|---|
| Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. |
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Graph Neural Networks...Artificial Intelligence and Machine Learning
| Brief | Book | |
|---|---|---|
| Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.
This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions. |
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Python Data Science...Tools for Working with Data
| Brief | Book | |
|---|---|---|
| For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. |
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First-order and Stochastic...for Machine Learning
| Brief | Book | |
|---|---|---|
| This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning. |
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Machine Learning...A Bayesian and Optimization Perspective
| Brief | Book | |
|---|---|---|
| This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. |
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Learning Python...5th Edition
| Book | Brief | |
|---|---|---|
| Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. |
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Machine Learning based Pairs Trading Investment Strategy
| Brief | Book | |
|---|---|---|
| This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder. |
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Data Mining...Concepts, Models, Methods, and Algorithms
| Book | Brief | |
|---|---|---|
| Advances in deep learning technology have opened an entire new spectrum of applications. The author―a noted expert on the topic―explains the basic concepts, models, and methodologies that have been developed in recent years.
This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter. Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field. |
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Statistical Machine Learning...A Unified Framework
| Brief | Book | |
|---|---|---|
| The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms.
This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. |
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Hands-On Machine Learning with R
| Book | Brief | |
|---|---|---|
| This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. |
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Machine Learning... A Quantitative Approach
| Brief | Book | |
|---|---|---|
| Machine learning is a newly-reinvigorated field. It promises to foster many technological advances that may improve the quality of our lives significantly, from the use of the latest, popular, high-gear gadgets such as smartphones, home devices, TVs, game consoles and even self-driving cars, and so on. Of course, for all of us in the circles of high education, academic research and various industrial fields, it offers more challenges and more opportunities. Whether you are a CS student taking a machine learning class or a scientist or an engineer entering the field of machine learning, this text helps you get up to speed with machine learning quickly and systematically. By adopting a quantitative approach, you will be able to grasp many of the machine learning core concepts, algorithms, models, methodologies, strategies and best practices within a minimal amount of time. Throughout the text, you will be provided with proper textual explanations and graphical exhibitions augmented not only with relevant mathematics for its rigor, conciseness, and necessity but also with high-quality examples. |
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Friday, August 21, 2020
Discovering Statistics Using IBM SPSS Statistics..
| Brief | Book | |
|---|---|---|
| With an exciting new look, math diagnostic tool, and a research roadmap to navigate projects, this new edition of Andy Field’s award-winning text offers a unique combination of humor and step-by-step instruction to make learning statistics compelling and accessible to even the most anxious of students. The Fifth Edition takes students from initial theory to regression, factor analysis, and multilevel modeling, fully incorporating IBM SPSS Statistics© version 25 and fascinating examples throughout. |
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Sunday, August 16, 2020
Data Mining for Business Analytics... Applications with XLMiner
| Book | Brief | |
|---|---|---|
| Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. | ||
R for Data Science...Import, Tidy, Transform, Visualize, and Model Data
| Brief | Book | |
|---|---|---|
| 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.
The authors 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:
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Federated Learning...Synthesis Lectures on #AI and #ML
| Book | Brief | |
|---|---|---|
| Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application. |
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Machine Learning Refined: Foundations, Algorithms, and Applications
| Book/th> | Brief | |
|---|---|---|
| With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study. |
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Designing Data-Intensive Applications...The Big Ideas Behind
| Brief | Book | |
|---|---|---|
| Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?
In this practical and comprehensive guide, the author helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. |
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Python for Data Analysis...Wrangling with Pandas, NumPy, and IPython
| Brief | Book | |
|---|---|---|
| Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. |
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Automated Machine Learning: Methods, Systems...
| Book | Brief | |
|---|---|---|
| This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. |
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Graph-Powered Machine Learning
| Book | Brief | |
|---|---|---|
| At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. |
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Machine Learning Meets Quantum Physics
| Brief | Book | |
|---|---|---|
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. |
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Interpretable Machine Learning...
| Brief | Book | |
|---|---|---|
| This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. | ||
Intro to Python for Computer Science and Data Science...
| Brief | Book | |
|---|---|---|
| Offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. |
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Probabilistic Graphical Models...Principles and Techniques
| Brief | Book | |
|---|---|---|
| Discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs. |
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The Pragmatic Programmer...Your Journey To Mastery
| Brief | Book | |
|---|---|---|
| Is one of those rare tech books you’ll read, re-read, and read again over the years. Whether you’re new to the field or an experienced practitioner, you’ll come away with fresh insights each and every time.
The first edition of this influential book in 1999 to help their clients create better software and rediscover the joy of coding. These lessons have helped a generation of programmers examine the very essence of software development, independent of any particular language, framework, or methodology, and the Pragmatic philosophy has spawned hundreds of books, screencasts, and audio books, as well as thousands of careers and success stories. Now, twenty years later, this new edition re-examines what it means to be a modern programmer. Topics range from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. |
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First-order and Stochastic Optimization Methods for Machine Learning
| Brief | Book | |
|---|---|---|
| This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning. |
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Machine Learning for Cybersecurity...using the Python ecosystem
| Brief | Book | |
|---|---|---|
| Organizations are increasingly vulnerable to many cybersecurity threats which can lead to significant financial losses, making smart data security more important than ever. In this book, you'll use different tools and techniques to solve a variety of significant problems that exist in the cybersecurity domain. The book begins by introducing you to the basics of machine learning in cybersecurity using Python and its libraries. You will then explore various machine learning domains, such as time series analysis and ensemble modeling. As you progress, you will implement various examples such as building a system to identify malicious URLs, and creating a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of the k-means algorithm to develop a solution for detecting and alerting you about any malicious activity in the network. I n addition to this, you'll get up to speed with implementing biometric authentication and fingerprint scanning to validate whether someone is a legitimate user or not. Finally, you will see how you can use TensorFlow for cybersecurity, along with understanding how deep learning is effective for creating models and training systems. |
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Data Mining for Business Analytics... Applications with JMP Pro
| Brief | Book | |
|---|---|---|
| Is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. |
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Machine Learning...Methods and Applications to Brain Disorders
| Book | Brief | |
|---|---|---|
| Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. |
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Computer Programming And Cyber Security...
| Brief | Book | |
|---|---|---|
This book won’t make you an expert programmer, but it will give you an exciting first look at programming and a foundation of basic concepts with which you can start your journey learning computer programming, machine learning and cybersecurity.
This book includes:
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Learning SQL... Generate, Manipulate, and Retrieve Data
| Brief | Book | |
|---|---|---|
| As data floods into your company, you need to put it to work right away—and SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports. You’ll find new chapters on SQL and big data, analytic functions, and working with very large databases. Each chapter presents a self-contained lesson on a key SQL concept or technique using numerous illustrations and annotated examples. Exercises let you practice the skills you learn. Knowledge of SQL is a must for interacting with data. With Learning SQL, you’ll quickly discover how to put the power and flexibility of this language to work.
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Machine Learning Design Patterns...Solutions to Common Challenges in MLOps
| Brief | Book | |
|---|---|---|
| The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation. | ||
Introduction to Data Mining and Analytics
| Book | Brief | |
|---|---|---|
| Provides a broad and interactive overview of a rapidly growing field. With a dual focus on concepts and operations, this text comprises a complete how-to and is an excellent resource for anyone considering the field. After defining the concepts of data mining and machine learning, Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use. The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world context through a practical application to the current issues of “big data” and of text and image data mining. The text concludes by describing an analyst’s steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace. |
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Machine Learning with the Elastic Stack...
| Brief | Book | |
|---|---|---|
| This book helps users ingest, process, and analyze search data effectively. With the flux of machine learning in its recent versions, Elastic Stack makes this process even more efficient. This book provides a comprehensive overview of Elastic Stack’s machine learning features for anomaly detection and forecasting.
Machine Learning with the Elastic Stack starts by guiding you in installing and setting up Elastic Stack. You’ll perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you’ll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you’ll see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you’ll be equipped with all the knowledge you need to incorporate machine learning in your distributed search solutions. |
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Data Mining for Business Analytics...Applications in Python
| Brief | Book | |
|---|---|---|
| Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.
This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis.
An ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. |
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Practical Statistics for Data Scientists...Using R and Python
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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. |
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Data Mining for Business Analytics...Concepts from R
| Brief | Book | |
|---|---|---|
| Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.
This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. This book is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. |
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Introduction to Data Mining...What's New in Computer Science
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| Introduction to Data Mining, 2nd Edition , gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth. |
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TinyML... Machine Learning with TensorFlow Lite
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|---|---|---|
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
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Machine Learning...A Bayesian and Optimization Perspective
| Book | Brief | |
|---|---|---|
| This book gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.
This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. |
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Machine Learning and Security...
| Book | Brief | |
|---|---|---|
| Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.
Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. |
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Fundamentals of Machine Learning for Predictive Data Analytics
| Brief | Book | |
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The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals. |
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Unsupervised Learning Using Python...
| Brief | Book | |
|---|---|---|
| Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.
Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production ready Python frameworks scikit learn and TensorFlow using Keras. With the hands on examples and code provided, you will identify difficult to find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. |
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Statistical Machine Learning...A Unified Framework
| Brief | Book | |
|---|---|---|
| The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. |
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Natural Language Processing in Action...
| Brief | Book | |
|---|---|---|
| Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside
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