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Machine Learning Concepts Techniques and Applications

Machine Learning Concepts Techniques and Applications

Machine Learning: Concepts Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases self-assessments exercises activities numerical problems and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare Education Business Engineering Applications Ethics of machine learning including Bias Fairness Trust Responsibility Basics of Deep learning important deep learning models and applications Plenty of objective questions Use Cases Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students researchers and professionals so that they can formulate the problems prepare data decide features select appropriate machine learning algorithms and do appropriate performance evaluation. | Machine Learning Concepts Techniques and Applications

GBP 140.00
1

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
1

Machine Learning Theory and Practice

Machine Learning Theory and Practice

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization tree-based methods including Random Forests and Boosted Trees Artificial Neural Networks including Convolutional Neural Networks (CNNs) reinforcement learning and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid illustrated with figures and examples. For each machine learning method discussed the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding enabling further exploration Presents worked out suitable programming examples thus ensuring conceptual theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth within limits of what can be taught in a short period of time. Thus the book can provide foundations that will empower a student to read advanced books and research papers. | Machine Learning Theory and Practice

GBP 110.00
1

Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation the authors explore how machine learning techniques apply to bioinformatics problems such as electron density map interpretation biclustering DNA sequence analysis and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

GBP 59.99
1

Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy suitability and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms architecture design and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model performance evaluation and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning along with recent research developments in healthcare sectors. | Machine Learning for Healthcare Handling and Managing Data

GBP 115.00
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

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. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification 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. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. 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. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
1

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas such as game development decision making and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning which is followed by Gaussian process introduction classification regression covariance and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates researchers and professionals in the field of data science and machine learning.

GBP 105.00
1

Applications of Optimization and Machine Learning in Image Processing and IoT

Applications of Optimization and Machine Learning in Image Processing and IoT

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals. The applications of machine learning and IoT are explored with topics including optimization algorithms and machine learning in image processing and IoT. Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss the fields of machine learning IoT and image processing. KEY FEATURES: • Includes fundamental concepts towards advanced applications in machine learning and IoT. • Discusses potential and challenges of machine learning for IoT and optimization • Reviews recent advancements in diverse researches on computer vision networking and optimization field. • Presents latest technologies such as machine learning in image processing and IoT This book has been written for readers in academia engineering IT specialists researchers industrial professionals and students and is a great reference for those just starting out in the field as well as those at an advanced level.

GBP 48.99
1

Statistical Reinforcement Learning Modern Machine Learning Approaches

Statistical Reinforcement Learning Modern Machine Learning Approaches

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence plant control and gaming the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches including model-based and model-free approaches policy iteration and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspectiveLays out the associated optimization problems for each reinforcement learning scenario coveredProvides thought-provoking statistical treatment of reinforcement learning algorithmsThe book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs as well as researchers and engineers in related fields. | Statistical Reinforcement Learning Modern Machine Learning Approaches

GBP 44.99
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Machine Learning Animated

Machine Learning Animated

The release of ChatGPT has kicked off an arms race in Machine Learning (ML) however ML has also been described as a black box and very hard to understand. Machine Learning Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning witnessing AI achieve super-human performance in Atari games such as Breakout Space Invaders Seaquest and Beam Rider. Written in a clear and concise style illustrated with animations and images this book is particularly appealing to readers with no background in computer science mathematics or statistics. Access the book's repository at: https://github. com/markhliu/MLA

GBP 74.99
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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Machine Learning a branch of Artificial Intelligence is influencing the society industry and academia at large. The adaptability of Python programming language to Machine Learning has increased its popularity further. Another technology on the horizon is Internet of Things (IoT). The present book tries to address IoT Python and Machine Learning along with a small introduction to Image Processing. If you are a novice programmer or have just started exploring IoT or Machine Learning with Python then this book is for you. Features: Raspberry Pi as IoT is described along with the procedure for installation and configuration. A simple introduction to Python Programming Language along with its popular library packages like NumPy Pandas SciPy and Matplotlib are dealt in an exhaustive manner along with relevant examples. Machine Learning along with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT is introduced to the audience who love to apply Machine Learning algorithms to Images The book follows hands-on approach and provide a huge collection of Python programs. | Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

GBP 44.99
1

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences

From the Foreword:While large-scale machine learning and data mining have greatly impacted a range of commercial applications their use in the field of Earth sciences is still in the early stages. This book edited by AshokSrivastava Ramakrishna Nemani and Karsten Steinhaeuser serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences. Vipin Kumar University of MinnesotaLarge-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science computer science statistics and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources in the final chapter of the book.

GBP 44.99
1

A First Course in Machine Learning

A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings and goes all the way to the frontiers of the subject such as infinite mixture models GPs and MCMC. —Devdatt Dubhashi Professor Department of Computer Science and Engineering Chalmers University Sweden This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade. —Daniel Barbara George Mason University Fairfax Virginia USA The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling inference and prediction providing ‘just in time’ the essential background on linear algebra calculus and probability theory that the reader needs to understand these concepts. —Daniel Ortiz-Arroyo Associate Professor Aalborg University Esbjerg Denmark I was impressed by how closely the material aligns with the needs of an introductory course on machine learning which is its greatest strength…Overall this is a pragmatic and helpful book which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months. —David Clifton University of Oxford UK The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process MCMC and mixture modeling provide an ideal basis for practical projects without disturbing the very clear and readable exposition of the basics contained in the first part of the book. —Gavin Cawley Senior Lecturer School of Computing Sciences University of East Anglia UK This book could be used for junior/senior undergraduate students or first-year graduate students as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective. —Guangzhi Qu Oakland University Rochester Michigan USA

GBP 39.99
1

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
1

Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
1

Deep and Shallow Machine Learning in Music and Audio

Deep and Shallow Machine Learning in Music and Audio

Providing an essential and unique bridge between the theories of signal processing machine learning and artificial intelligence (AI) in music this book provides a holistic overview of foundational ideas in music from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place this book explores how sound may be represented and manipulated by computer systems and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory without the frustrations of free-form coding. Surveying state-of-the art methods in applications of deep neural networks to audio and sound computing as well as offering a research perspective that suggests future challenges in music and AI research this book appeals to both students of AI and music as well as industry professionals in the fields of machine learning music and AI. | Deep and Shallow Machine Learning in Music and Audio

GBP 44.99
1

Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing

Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing

The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. Advances in Complex Decision Making: Using Machine Learning and Tools for Service-Oriented Computing is a cutting-edge technical guide exploring the latest decision-making technology advancements. This book provides a comprehensive overview of machine learning algorithms and examines their applications in complex decision-making systems in a service-oriented framework. The authors also delve into service-oriented computing and how it can be used to build complex systems that support decision making. Many real-world examples are discussed in this book to provide a practical insight into how discussed techniques can be applied in various domains including distributed computing cloud computing IoT and other online platforms. For researchers students data scientists and technical practitioners this book offers a deep dive into the current developments of machine learning algorithms and their applications in service-oriented computing. This book discusses various topics including Fuzzy Decisions ELICIT OWA aggregation Directed Acyclic Graph RNN LSTM GRU Type-2 Fuzzy Decision Evidential Reasoning algorithm and robust optimisation algorithms. This book is essential for anyone interested in the intersection of machine learning and service computing in complex decision-making systems. | Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing

GBP 44.99
1

Machine Learning in Signal Processing Applications Challenges and the Road Ahead

Machine Learning in Signal Processing Applications Challenges and the Road Ahead

Machine Learning in Signal Processing: Applications Challenges and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML as the driving force of the wave of artificial intelligence (AI) provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML. The focus is on understanding the contributions of signal processing and ML and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams This book is a complete resource designed exclusively for advanced undergraduate students post-graduate students research scholars faculties and academicians of computer science and engineering computer science and applications and electronics and telecommunication engineering. | Machine Learning in Signal Processing Applications Challenges and the Road Ahead

GBP 150.00
1

Data Science AI and Machine Learning in Drug Development

Data Science AI and Machine Learning in Drug Development

The confluence of big data artificial intelligence (AI) and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information Data Science AI and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D emerging applications of big data AI and ML in drug development and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data AI and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise | Data Science AI and Machine Learning in Drug Development

GBP 99.99
1

Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods data exploration with predictive models penalized regressions model selection with sparsity dimension reduction methods nonparametric time-series predictions graphical network analysis algorithmic optimization methods classification with imbalanced data and many others. This book is targeted at students and researchers who have no advanced statistical background but instead coming from the tradition of inferential statistics. The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields. Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in estimation and prediction for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It’s self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions Dimension Reduction Methods and Graphical Methods have been increasing in their popularity in social sciences. | Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

GBP 74.99
1

Machine Learning for Factor Investing: R Version

Machine Learning for Factor Investing: R Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material along with the content of the book is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

GBP 66.99
1

Transformers for Machine Learning A Deep Dive

Transformers for Machine Learning A Deep Dive

Transformers are becoming a core part of many neural network architectures employed in a wide range of applications such as NLP Speech Recognition Time Series and Computer Vision. Transformers have gone through many adaptations and alterations resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech text time series and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students practitioners and professionals as it allows for quick experimentation and lowers the barrier to entry into the field. | Transformers for Machine Learning A Deep Dive

GBP 44.99
1

Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology

Molecular biologists are performing increasingly large and complicated experiments but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs ortholog identification motif finding inference of population structure protein fold prediction and many more. The book takes a pragmatic approach focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics. | Statistical Modeling and Machine Learning for Molecular Biology

GBP 180.00
1