Python machine learning

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, fin

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, th

This book delves into the design and implementation of IoT applications, showing how data analytics, machine learning, and AI enhance IoT efficiency and innovation. With a focus on automation, decision-making, and connectivity, it tackles challenges in various domains including healthcare, agriculture, and industrial automation.

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics and machine learning. Presenting interesting, new experimental results, "Learni

Advanced statistical modeling and knowledge representation techniques for a newlyemerging area of machine learning and probabilistic reasoning; includes introductory material,tutorials for different p

Discover the Most Comprehensible Beginner's Guide to Coding for Children, Packed with Fun Coding Activities and Games All Kids Will LoveDear friend, Do you have a little smartypants running around your home? Would you like to ensure the brightest possible future for your child? If so, then this book is a perfect choice for both of you.This bundle is an excellent choice for all children who are interested in the world of computers, programming, and coding. It is specially made for kids aged from 8 to 12 that have no prior knowledge of coding.Here is what this bundle can teach your child: Game-based learning - there's no better way for kids to learn than through playing and fun activities that will capture your child's attention.40+ fun coding activities and games - this bundle is packed with more than 40 fun activities that will introduce coding to your child and help them grasp the basic skills from a very young age.Easy-to-follow guidance - Straightforward directions and tips keep you

Packed with detailed examples and illustrations, PRECISION MACHINING TECHNOLOGY, Third Edition, is your ideal introduction to today's machine tool industry, equipping you with a solid understanding of

Microdermabrasion is designed to support your career by showing you step-by-step both a manual microdermabrasion treatment and a machine-aided microdermabrasion. The content covers an overview of trea

David and his family use his grandfather's time travel machine to travel to 1893 Cambridge, Massachusetts, where they meet African American football pioneer William Henry Lewis, who played for Harvard

Lymphatic Drainage introduces you to the simple-to-learn techniques of lymph drainage massage in two phases, starting with treating the neck and then the face. Covering both manual and machine-aided l

Cybersecurity attacks grow more complex every day. The first edition of Wilson's COMPTIA PENTEST+ GUIDE TO PENETRATION TESTING helps you gain the knowledge and skills necessary to analyze systems in the same way hackers do. It provides the information you need to successfully pass the CompTIA PenTest+ PTO-002 certification exam and contains detailed examples, projects, and hands-on activities (including virtual machine labs and interactive online features) where you can practice using the actual tools and methodologies needed to be a successful penetration tester. The author begins by exploring the concept of penetration testing, and then walks you through the process of building your own penetration testing lab of virtual machines (hosted on your own computer, which is used to practice the use of pen testing tools and methods). A final project guides you in the creation of a penetration testing report that includes all the areas covered in earlier modules.

Develop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, 5E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals clearly illustrate data and results. Step-by-step instructions guide you through using Excel, Tableau, R or the Python-based Orange data mining software to perform advanced analytics. Practical, relevant problems at all levels of difficulty let you apply what you've learned. Updates throughout this edition address topics beyond traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's business environment. MindTap and WebAssign online learning platforms are also available with an interactive eBook, algorithmic practice problems and Exploring Analytics visualizations to strengthen your understanding of key concepts.

這是一本介紹如何應用大數據和機器學習技術來解決投資問題並提高投資績效的專著。近年來，機器學習和數據科學在投資中發揮著越來越大的作用。從本質上講，機器學習模型在挖掘數據背後的非線性關係上要強于傳統模型。借助機器學習和大數據，投資經理能夠做出以往傳統模型無法實現的預測，進而做出明智的決策。然而，在投資中，並不是所有的數據集和機器學習技術都對金融應用有用，也不是所有的機器學習技術都可以“即插即用”。《大數據、機器學習與量化投資》這本書由資深量化分析專家托尼‧吉達主編，彙集了多位業內頗具影響力的專家學者的前沿分享，闡釋如何應用大數據和機器學習技術來解決投資問題並提高投資績效。這本書共有13章，理論嚴謹，案例豐富，內容涵蓋機器學習在投資管理中的應用現狀和前景、另類數據和大數據在宏觀交易中的應用、處理大數據集的難點和解決方案、挖掘社交媒體數據集分析企業文化、使用自然語言處理技術進行投資者情緒分析、基於支持向量回歸的全球戰術性資產配置策略、強化學習和深度學習在投資組合優化中的應用等主題，可以作為量化投資從業者、金融算法研究人員、高等院校計算機專業和金融工程專業的師生以及機器學習愛好者的參考用書。

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming in

Master the lucrative discipline of quantitative trading with this insightful handbook from a master in the field In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm.You'll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as: Updated back tests on a variety of trading strategies, with included Python and R code examplesA new technique on optimizing parameters with changing market regimes using machine learning. A guide to selecting the best traders and advisors to manage your money Perfect for independent retail traders seeking to start their own quantitative trading business, or investors looking to invest in such traders, this new edition of Quantitative Trading will also earn a

This comprehensive and timely book, New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling, explores the importance of tools and techniques used in machine lear

This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural l

Recent startling successes in machine intelligence using a technique called ‘deep learning’ seem to blur the line between human and machine as never before. Are computers on the cusp of be

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts a

Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy.Uncertainty Modeling for Data Mining: A Label Se

This book celebrates the work of Vladimir Vapnik, developer of the support vector machine, which combines methods from statistical learning and functional analysis to create a new approach to learning

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical

This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computatio

This work combines interdisciplinary knowledge and experience from research fields of psychology, linguistics, audio-processing, machine learning, and computer science. The work systematically explore

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation a

Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive sea

1.Covers not only AI theory, but modern applications e.g., game programming, machine learning, swarming, artificial immune systems, genetic algorithms, pattern recognition, numerical optimization, dat

Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of expe

Digitization is reshaping our careers, companies, and industries. Machine learning, artificial intelligence, and robotics are surpassing human capabilities across broad sectors of the economy but also

This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform gener