Python machine learning

This interactive early learning activity book is packed full of trains! Trains that huff and puff and rattle and rush. Trains that can rumble over bridges and through tunnels. Trains with rails and wh

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comp

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for c

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, f

Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit te

What's the point of art?This is the question Henri asks himself when his class is taken on a school trip to the local art gallery. Who wants pictures of old soup cans or melting watches? But when he encounters a painting with the most perfect blue, it creates a beautiful feeling. It's blueberries, oceans, summer skies and butterflies all rolled into one.And then he wanders into a room with a chair and a sign that reads "Is this really a chair?" Henri decides to test it out, unleashing an explosion of creativity! Finally Henri understands what art is all about... Following Isabelle Marinov's successful Leo and the Octopus, this is a fun and encouraging story of a boy learning to appreciate art and its boundless creativity.

Learning Python just got fun for kids!Learning to code is just like playing a new sport or practicing an instrument—just get started! From the basic building blocks of programming to creating your ver

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the w

Foreword by Oliver Schabenberger, PhDExecutive Vice President, Chief Operating Officer and Chief Technology Officer SASDive into deep learning! Machine learning and deep learning are ubiquitous in our

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identificati

This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the clas

It's nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and rei

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues

An illuminating dive into the latest science on our brain’s remarkable learning abilities and the potential of the machines we program to imitate themThe human brain is an extraordinary machine. Its a

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.

"There are words that are so familiar they obscure rather than illuminate the thing they mean, and 'learning' is such a word. It seems so ordinary, everyone does it. Actually it's more of a black box,

This book constitutes the refereed proceedings of the IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018, held in Hamburg, G

Anew title in The QuestKids children's series thatwas launched by In Easy Steps in 2018, designed to make learning fun forchildren.Coding with Python- Create Amazing Graphics introducescoding in Python through a variety of projects. Each one teaches new codingconcepts and results in some amazing graphics.Discover how to mix colours, make patterns, draw 2D and 3Dshapes.Learn how to use loops, variables, random numbers, andfunctions in Python. Packed with challenges, tips, and extensions to keep allbudding coders engaged and eager to keep learning and creating!To create the games in this book, children will need a desktopcomputer or a laptop. The games require a proper keyboard so will not work wellon a tablet or iPad. It is recommended that children should be supervised whenusing the internet, especially when using a new website. Thisis the UK English edition.

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python. What You Will LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems Who This Book Is ForEntry-level to mid-level Python developers from various backg

Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduct

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

As an introduction to Python, this book allows readers to take a slow and steady approach to understanding Python code, explaining concepts, connecting programming with real-life examples, writing Python programs, and completing case studies.While there are many books, websites, and online courses about the topic, we break down Python programming into easily digestible lessons of less than 5 minutes each, following our BiteSize approach. Each lesson begins with a clear and short introduction to the topic. This gives you a strong base to start from and gets you ready for deeper learning. Then, you will see coding demonstrations that show the ideas discussed. These examples are simple and useful, helping you really understand the concepts. You'll then practice tasks at different difficulty levels, so you can test your knowledge and increase your confidence. You'll also play with case studies to solve real-world problems. Tips are included to show how you can incorporate generative AI int

A detailed examination of learning classifier systems (LCS), a form of machine learning system, which incorporates both Evolutionary Algorithms and Reinforcement Learning Algorithms.