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R 1,114.80
Introduction to Machine Learning Algorithms
Mathematics is the foundation of machine learning algorithms. To understand the shortcomings of existing algorithms and develop more effective methods, it is essential to understand the mathematical concepts underlying these algorithms and their operational principles. This book serves as an introductory resource, outlining the preliminary concepts and offering insights into the mathematical foundations and operational mechanisms of machine learning algorithms. It describes the basic equations and interrelates the questions arising during practical applications of machine learning with the basic mathematical picture of the algorithms used.• Introduces machine learning, highlights the central role of algorithms in machine learning, and explains the core mathematical prerequisites to understanding machine learning algorithms• Systematically examines the sequential steps of classical machine learning algorithms used for classification of data sets into distinct groups; regression, clustering analysis,• Provides an overview of value, policy, and model-based reinforcement learning algorithms. This book is for academicians, scholars, students, and professionals engaged in the study of machine learning and artificial intelligence.
R 9,342.00
R 409.00
Information-Driven Machine Learning
This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline's robustness and credibility.While bridging the gap between machine learning and disciplines such as physics, information theory, and computer engineering, this textbook maintains an accessible and comprehensive style, making complex topics digestible for a broad readership. Information-Driven Machine Learning explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the "how," this text provides answers to the "why" questions that permeate the field, shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles, this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics, including deep learning, data drift, and MLOps, using fundamental principles such as entropy, capacity, and high dimensionality.Ideal for both academia and industry professionals, this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who desire more than just code or ready-made formulas. The text invites readers to explore beyond conventional viewpoints, offering an alternative perspective that promotes a big-picture view for integrating theory with practice. Suitable for upper undergraduate or graduate-level courses, this book can also benefit practicing engineers and scientists in various disciplines by enhancing their understanding of modeling and improving data measurement effectively.
R 3,114.00
R 456.00
Machine Learning: From Theory to Applications
This volume includes some of the key research papers in thearea of machine learning produced at MIT and Siemens duringa three-year joint research effort. It includes papers onmany different styles of machine learning, organized intothree parts.Part I, theory, includes three papers on theoretical aspectsof machine learning. The first two use the theory ofcomputational complexity to derive some fundamental limitson what isefficiently learnable. The third provides anefficient algorithm for identifying finite automata.Part II, artificial intelligence and symbolic learningmethods, includes five papers giving an overview of thestate of the art and future developments in the field ofmachine learning, a subfield of artificial intelligencedealing with automated knowledge acquisition and knowledgerevision.Part III, neural and collective computation, includes fivepapers sampling the theoretical diversity and trends in thevigorous new research field of neural networks: massivelyparallel symbolic induction, task decomposition throughcompetition, phoneme discrimination, behavior-basedlearning, and self-repairing neural networks.
R 1,554.75
R 466.00
Mastering Machine Learning Algorithms
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problemsKey FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook DescriptionMastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is forThis book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
R 1,661.00
Machine Learning for Decision Sciences with Case Studies in Python
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
R 11,219.02
Practical Machine Learning with Python
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!What You'll LearnExecute end-to-end machine learning projects and systemsImplement hands-on examples with industry standard, open source, robust machine learning tools and frameworksReview case studies depicting applications of machine learning and deep learning on diverse domains and industriesApply a wide range of machine learning models including regression, classification, and clustering.Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is ForIT professionals, analysts, developers, data scientists, engineers, graduate students
R 2,931.00
Machine Learning For Dummies
One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learningWork with Python 3.8 and TensorFlow 2.x (and R as a download)Build and test your own modelsUse the latest datasets, rather than the worn out data found in other booksApply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.
R 798.00
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
R 4,066.00
Introduction to Machine Learning
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic;Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning;Not tied to any specific software language or hardware implementation.
R 4,184.22
R 792.86
Trustworthy Machine Learning under Imperfect Data
The subject of this book centresaround trustworthy machine learning under imperfect data. It is primarily designed forscientists, researchers, practitioners, professionals, postgraduates andundergraduates in thefield of machine learning and artificial intelligence. The book focuseson trustworthy deep learning under various types of imperfect data, includingnoisy labels, adversarial examples, and out-of-distribution data. It coverstrustworthy machine learning algorithms, theories, and systems. The main goal of the book is to provide students and researchers in academia with anunbiased and comprehensive literature review. More importantly, it aims to stimulateinsightful discussions about the future of trustworthy machine learning. By engaging the audiencein more in-depth conversations, the book intends to spark ideas for addressing coreproblems in this topic. For example, it will explore how to build up benchmark datasets innoisy-supervised learning, how to tackle the emerging adversarial learning, andhow to tackle out-of-distribution detection. For practitioners in the industry, this book will present state-of-the-art trustworthy machine learning methods tohelp them solve real-world problems in different scenarios, such as onlinerecommendation and web search. While the book will introduce the basics ofknowledge required, readers will benefit from having some familiarity withlinear algebra, probability, machine learning, and artificial intelligence. Theemphasis will be on conveying the intuition behind all formal concepts, theories, and methodologies, ensuring the book remains self-contained at a highlevel.
R 7,583.62
Scalable and Distributed Machine Learning and Deep Learning Patterns
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
R 10,420.70
R 409.00
R 549.18
Artificial Intelligence, Machine Learning, and Deep Learning
This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learningclassifiers such as logistic regression, k-NN, decision trees, random forests,and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs,LSTMs, and auto encoders. Keras-based code samples are included to supplementthe theoretical discussion. In addition, this book contains appendices forKeras, TensorFlow 2, and Pandas.
R 1,527.00
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,588.56
Data Science and Machine Learning
Praise for the first edition:“In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science.”- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6“This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely—very useful for readers who wish to understand the rationale and flow of the background knowledge.”- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization–Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
R 4,066.00
R 428.00
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.
R 5,165.00
Combinatorial Machine Learning
Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection. The aims of this book are (i) the consideration of the setsof decision trees, rules and reducts; (ii) study of relationships among theseobjects; (iii) design of algorithms for construction of trees, rules and reducts;and (iv) obtaining bounds on their complexity. Applications for supervisedmachine learning, discrete optimization, analysis of acyclic programs, faultdiagnosis, and pattern recognition are considered also. This is a mixture ofresearch monograph and lecture notes. It contains many unpublished results.However, proofs are carefully selected to be understandable for students.The results considered in this book can be useful for researchers in machinelearning, data mining and knowledge discovery, especially for those who areworking in rough set theory, test theory and logical analysis of data. The bookcan be used in the creation of courses for graduate students.
R 3,021.03
Machine Learning and Deep Learning Using Python and TensorFlow
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Explore the principles and practices of machine learning and deep learning This comprehensive textbook lays out the theories and applications of machine learning and deep learning in a style that is approachable for students and working professionals at all math skill levels. You will discover how to handle data, regression and logistic regression, decision trees, cross-validation techniques and error testing, artificial neural networks (ANN, CNN and RNN), random forests, boosting, and more. Machine Learning and Deep Learning Using Python and TensorFlow includes valuable case studies across professional domains, including banking, insurance, e-commerce, retail, and healthcare. The book presents examples using both Python and TensorFlow programming languages and clearly demonstrates how to utilize this cutting-edge technology in today’s “smart” devices.• Presented in a mathematically non-rigorous way• Includes image classification and computer vision tasks • Written by a pair of business analytics experts
R 4,616.00
Machine Learning with BigQuery ML
Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery MLKey FeaturesGain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery MLLeverage SQL syntax to train, evaluate, test, and use ML modelsDiscover how BigQuery works and understand the capabilities of BigQuery ML using examplesBook DescriptionBigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.What you will learnDiscover how to prepare datasets to build an effective ML modelForecast business KPIs by leveraging various ML models and BigQuery MLBuild and train a recommendation engine to suggest the best products for your customers using BigQuery MLDevelop, train, and share a BigQuery ML model from previous parts with AI Platform NotebooksFind out how to invoke a trained TensorFlow model directly from BigQueryGet to grips with BigQuery ML best practices to maximize your ML performanceWho this book is forThis book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.
R 1,661.00
Data Mining and Machine Learning
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
R 3,573.40
Python Machine Learning By Example
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook DescriptionPython Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is forIf you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
R 1,335.00
Advanced Lectures on Machine Learning
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600.This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references.Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
R 1,549.76
R 310.00
Machine Learning Paradigms
At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
R 5,617.63
R 570.00
Machine Learning in Medicine
Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems.New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding.This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.
R 9,953.91
Hands-On Machine Learning with TensorFlow.js
Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectivelyKey FeaturesBuild, train and run machine learning models in the browser using TensorFlow.jsCreate smart web applications from scratch with the help of useful examplesUse flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms functionBook DescriptionTensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach.Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge.By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.What you will learnUse the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input datasetDeploy tfjs-converter to convert Keras models and load them into TensorFlow.jsApply the Bellman equation to solve MDP problemsUse the k-means algorithm in TensorFlow.js to visualize prediction resultsCreate tf.js packages with Parcel, Webpack, and Rollup to deploy web appsImplement tf.js backend frameworks to tune and accelerate app performanceWho this book is forThis book is for web developers who want to learn how to integrate machine learning techniques with web-based applications from scratch. This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow.js. Working knowledge of JavaScript programming language is all you need to get started.
R 1,661.00
Advancements in Machine Learning
This CCIS 2538 volume constitutes the proceedings of First and Second International Conference on Advancements in Machine Learning, ICCAML 2024, in Pune, India, during February 28-29, 2024. The 19 full papers are carefully reviewed and selected from 173 submissions. They were cartegorized under the topical sections as follows: i) Modernization in Machine Learning ii) Machine Learning for Data Analytics and iii) Machine Learning for automation
R 3,883.65
R 4,507.00
Machine Learning
Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount to specific design choices for the model, data, and loss of a ML method.
R 2,381.00
R 5,465.00
R 279.00
R 418.00
Mathematical Theories of Machine Learning - Theory and Applications
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.
R 4,197.43
Machine Learning Workshop
Take a comprehensive and step-by-step approach to understanding machine learningKey FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook DescriptionMachine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms.The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you’ll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one.By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.What you will learnUnderstand how to select an algorithm that best fits your dataset and desired outcomeExplore popular real-world algorithms such as K-means, Mean-Shift, and DBSCANDiscover different approaches to solve machine learning classification problemsDevelop neural network structures using the scikit-learn packageUse the NN algorithm to create models for predicting future outcomesPerform error analysis to improve your model's performanceWho this book is forThe Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.
R 1,335.00
Scaling up Machine Learning
This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.
R 2,589.23
Machine Learning with PyTorch and Scikit-Learn
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnExplore frameworks, models, and techniques for machines to 'learn' from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.
R 1,791.00
