ai and machine learning for coders laurence moroney

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

Objev podobné jako AI and Machine Learning For Coders - Laurence Moroney

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

Objev podobné jako Data Science and Machine Learning for Non-Programmers - Dothang Truong

A comprehensive guide for aspiring and current AI product managers The AI Product Playbook: Strategies, Skills, and Frameworks for the AI-Driven Product Manager, by Dr. Marily Nika and Diego Granados, is a practical resource designed to empower product managers to effectively build, launch, and manage successful AI-powered products. This playbook bridges the gap between artificial intelligence theory and real-world product management, offering actionable learnings tailored to non-technical professionals. Drawing from extensive industry experience, Dr. Nika and Granados introduce the three essential AI product manager roles: AI Experiences PM, AI Builder PM, and AI-Enhanced PM. They offer guidance on developing skills crucial for each role and navigating common challenges in the workplace. Readers will also find valuable strategies for career growth, lifelong learning, and crafting a distinctive AI portfolio. Inside the book: Practical frameworks for discovering AI opportunities and aligning AI capabilities with business goalsA deep technical dive with clear explanations of foundational AI and machine learning concepts, including supervised learning, unsupervised learning, reinforcement learning, and generative AIGuidelines for ethical AI implementation, addressing bias, fairness, and compliance with AI regulationsStrategies for effective collaboration with cross-functional teams and enhancing productivity through AIInteractive exercises, action plans, checklists, templates, and quizzes designed to reinforce ... Unknown localization key: "more"

Objev podobné jako The AI Product Playbook - Marily Nika, Diego Granados

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This book offers a comprehensive introduction to AI and Machine Learning fundamentals, equipping software testers with the skills to effectively leverage AI-powered solutions for testing complex systems and AI applications. It also fully prepares readers for the ISTQB® Certified Tester AI Testing (CT-AI) certification exam. Written in a practical and accessible way, this book offers a structured approach to AI testing based on the ISTQB® CT-AI syllabus. This book is filled with practical examples, detailed explanations, industry insights, a mock exam and chapter-based questions to prepare readers for passing the ISTQB® CT-AI exam to obtain certification.The materials are designed for both university graduates and practitioners involved in testing AI-based systems or those seeking a deeper understanding of AI systems in general. It offers valuable take on into implementing AI-enabled solutions and enhancing the quality of AI-driven software, making it an indispensable resource.

Objev podobné jako Introduction to AI Testing - Anna-Maria Lukina, Iuliia Emelianova, Iosif Itkin, Dmitrii Degtiarenko

Operational AuditingOperational Auditing: Principles and Techniques for a Changing World, 2nd edition, explains the proven approaches and essential procedures to perform risk-based operational audits. It shows how to effectively evaluate the relevant dynamics associated with programs and processes, including operational, strategic, technological, financial and compliance objectives and risks. This book merges traditional internal audit concepts and practices with contemporary quality control methodologies, tips, tools and techniques. It explains how internal auditors can perform operational audits that result in meaningful findings and useful recommendations to help organizations meet objectives and improve the perception of internal auditors as high-value contributors, appropriate change agents and trusted advisors. The 2nd edition introduces or expands the previous coverage of: • Control self-assessments. • The 7 Es framework for operational quality. • Linkages to ISO 9000. • Flowcharting techniques and value-stream analysis • Continuous monitoring. • The use of Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs). • Robotic process automation (RPA), artificial intelligence (AI) and machine learning (ML); and • Adds a new chapter that will examine the role of organizational structure and its impact on effective communications, task allocation, coordination, and operational resiliency to more effectively respond to market demands.Auditor EssentialsInternal auditors must ... Unknown localization key: "more"

Objev podobné jako The Change Agent, Auditor Essentials, and Operational Auditing Three-Book Set - Hernan Murdock

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Objev podobné jako Machine Learning For Dummies - John Paul Mueller, Luca Massaron

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics - Peter Gedeck, Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Inbal Yahav

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of "black-box" in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially ... Unknown localization key: "more"

Objev podobné jako Human and Machine Learning

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learningA new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Kuber R. Deokar

Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.

Objev podobné jako Machine Learning for Managers - Paul Geertsema

Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical and computational sciences for years to come.  Â

Objev podobné jako Multivariate Statistics and Machine Learning - Daniel J. Denis

This book is a comprehensive guide suitable for beginners and experienced developers alike. It teaches readers how to master object-oriented programming (OOP) with Python and use it in real-world applications.Start by solidifying your OOP foundation with clear explanations of core concepts like use cases and class diagrams. This book goes beyond theory as you get practical examples with well-documented source code available in the book and on GitHub.This book doesn''t stop at the basics. Explore how OOP empowers fields like data persistence, graphical user interfaces (GUIs), machine learning, and data science, including social media analysis. Learn about machine learning algorithms for classification, regression, and unsupervised learning, putting you at the forefront of AI innovation.Each chapter is designed for hands-on learning. You''ll solidify your understanding with case studies, exercises, and projects that apply your newfound knowledge to real-world scenarios. The progressive structure ensures mastery, with each chapter building on the previous one, reinforced by exercises and projects.Numerous code examples and access to the source code enhance your learning experience. This book is your one-stop shop for mastering OOP with Python and venturing into the exciting world of machine learning and data science.

Objev podobné jako The Object-Oriented Approach to Problem Solving and Machine Learning with Python - Maha Hadid, Sujith Samuel Mathew, Shahbano Farooq, Mohammad Amin K

A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the ... Unknown localization key: "more"

Objev podobné jako Applied Machine Learning for Data Science Practitioners - Vidya Subramanian

This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.

Objev podobné jako Machine Learning for Computer Scientists and Data Analysts - Houman Homayoun, Zhiqian Chen, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Text - Charu C. Aggarwal

This textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.

Objev podobné jako A Guide to Applied Machine Learning for Biologists

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Objev podobné jako The Statistical Physics of Data Assimilation and Machine Learning - Henry D. I. Abarbanel

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Objev podobné jako Pattern Recognition and Machine Learning - Christopher M. Bishop

Machine Learning for Kids introduces young readers to the concept of Artificial Intelligence (AI) and the related applications of Machine Learning. Readers learn how to create intelligent games like a rock-paper-scissors game that can learn hand motions and use them to compete against another player; a post sorting game that will recognise the postal code on an envelope and use it to send a letter to the right place. Each project demonstrates a different way that AI is used in the real-world, and readers will be introduced to the biggest issues and challenges that the adoption of AI brings to society.

Objev podobné jako Machine Learning for Kids - Dale Lane

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Objev podobné jako Pattern Recognition and Machine Learning - Christopher M. Bishop

Unlock the potential of AI in your organization with this updated, must-read guide, and discover how to leverage AI to drive innovation, improve efficiency, and gain a competitive edge.This comprehensive guide dives into the latest AI ethics legislation, explore advanced robotics and machine learning, and discover how AI enhances human capabilities. Gain powerful insights into today’s AI applications and learn how emerging trends could reshape industries while addressing critical ethical challenges. Perfect for beginners and strategists alike, this book is your launchpad to crafting a forward-thinking AI strategy that meets modern standards and keeps your organization on the cutting edge of responsible AI. The new edition aligns to the updated BCS Certification in AI Foundation and Essentials, which this book supports as essential reading.

Objev podobné jako Artificial Intelligence and Machine Learning Foundations - Andrew Lowe, Steve Lawless

This book is a concise but thorough introduction to the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression.

Objev podobné jako Fundamentals of Pattern Recognition and Machine Learning - Ulisses Braga-Neto

This book explores the purpose, role and function of the university and examines the disconnection between studentsÂ’ approaches to learning and university strategy. It centres on the idea that it is vital to explore what counts as a university in the twenty-first century, what it is for, and for whom, as well as how it can transcend social divisions. The universities of the twenty-first century need to have larger audiences, a broader voice, a shift away from othering and an effective means of progressing such shifts. What is central to such exploration is the idea that learning needs to be seen as postdigital. With a focus on how the growth of technology has and continues to affect university learning, this book:explores the concepts of the digital and the postdigitalpromotes just and inclusive pedagogies for higher educationconsiders ways to ensure learning is an ethical and political experiencestudies how to understand community and collective values through higher educationsuggests ways of promoting personal and collective responsibility for our world and its peoplespresents ways in which the university can challenge ideologies based on capitalist modes of consumption, privilege and exploitationDigital and Postdigital Learning for Changing Universities is essential reading for anyone seeking to reimagine ... Unknown localization key: "more"

Objev podobné jako Digital and Postdigital Learning for Changing Universities - Savin-Baden Maggi

Whether they are new or experienced, teachers are expected to plan and deliver high-quality computing lessons to their pupils. Computing and Digital Learning for Primary Teachers provides an accessible introduction to teaching computing effectively and for deeper understanding in the primary classroom.Filled with practical resources to support lesson design, long-term planning, and assessment, readers will benefit from building their subject knowledge and learning to create engaging lessons for their pupils. Chapters explore:Supporting computational thinking and problem-solving to teach our pupils how to solve problems logically and systematically.Developing pupils’ digital literacy and use of IT, creating exciting opportunities for children’s digital self-expression through film, animation, and 3D design.Managing technology in our schools, such as setting up and maintaining a virtual learning environment (VLE).Cross-curriculum links with STEAM and engineering, allowing children to solve real-world problems by combining their digital literacy with their knowledge of maths, science, and technology.Cost-effective and accessible ways of introducing physical computing and robotics to children.Safe and responsible uses of artificial intelligence (AI) in our primary schools.This essential resource provides a highly practical guide to delivering effective computing lessons in the primary classroom and is a must read for anyone who wishes to become a more confident and knowledgeable ... Unknown localization key: "more"

Objev podobné jako Computing and Digital Learning for Primary Teachers - Owen Dobbing

By moving away from flawed statistical methodologies, you'll move toward an intuitive view of probability as a mathematically rigorous statistical framework that quantifies uncertainty holistically and successfully. This book shows you how.

Objev podobné jako Probabilistic Machine Learning for Finance and Investing - Deepak K. Kanungo

Will technology change what it means to be human?You don''t have to be a computer scientist to have discerning conversations about artificial intelligence and technology. We all wonder where we''re headed. Even now, technological innovations and machine learning have a daily impact on our lives, and many of us see good reasons to dread the future. Are we doomed to the surveillance society imagined in George Orwell''s 1984?Mathematician and philosopher John Lennox believes that there are credible responses to the daunting questions that AI poses, and he shows that Christianity has some very serious, sensible, evidence-based things to say about the nature of our quest for superintelligence.This newly updated and expanded edition of 2084 will introduce you to a kaleidoscope of ideas:Key recent developments in technological enhancement, bioengineering, and, in particular, artificial intelligence.Consideration of the nature of AI systems with insights from neuroscience.The way AI is changing how we communicate, implications for medicine, manufacturing and the military, its use in advertising and automobiles, and education and the future of work.How data is used today for surveillance, thought control.The rise of virtual reality and the metaverse.The transhumanist agenda and longtermism.The agreements and disagreements that scientists and experts have about the future ... Unknown localization key: "more"

Objev podobné jako 2084 and the AI Revolution, Updated and Expanded Edition - John Lennox C.

This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.

Objev podobné jako Graph-Powered Analytics and Machine Learning with TigerGraph - Ph.D., Victor Lee, Xinyu Chang, Phuc Kien Nguyen

The next generation of AI is here—use it to lead your business forward.If you read nothing else on artificial intelligence and machine learning, read these 10 articles. We''ve combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.This book will inspire you to:Create a new AI strategyLearn to work with intelligent robotsGet more from your marketing AIBe ready for ethical and regulatory challengesUnderstand how generative AI is game changingStop tinkering with AI and go all inThis collection of articles includes "Competing in the Age of AI," by Marco Iansiti and Karim R. Lakhani; "How to Win with Machine Learning," by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; "Developing a Digital Mindset," by Tsedal Neeley and Paul Leonardi; "Learning to Work with Intelligent Machines," by Matt Beane; "Getting AI to Scale," by Tim Fountaine, Brian McCarthy, and Tamim Saleh; "Why You Aren''t Getting More from Your Marketing AI," by Eva Ascarza, Michael Ross, and Bruce G. S. Hardie; "The Pitfalls of Pricing Algorithms," by Marco Bertini and Oded Koenigsberg; "A Smarter Strategy for Using Robots," ... Unknown localization key: "more"

Objev podobné jako HBR's 10 Must Reads on AI - Harvard Business Review, Ajay Agrawal, Tsedal Neeley, Thomas H. Davenport, Marco Iansiti

If you''ve been curious about artificial intelligence and machine learning but didn''t know where to start, this is the book you''ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you''ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models'' performance. You''ll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they''re trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You''ll conduct experiments along the way, building to a final case study that incorporates everything you''ve learned. This second edition is thoroughly revised and updated, and adds ... Unknown localization key: "more"

Objev podobné jako Practical Deep Learning, 2nd Edition - Ronald T. Kneusel

Engage the World Change the World Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, whole-system change and transform learning for all students. Deep Learning is a global partnership that works to: transform the role of teachers to that of activators who design experiences that build global competencies using real-life problem solving; and supports schools, districts, and systems to shift practice and how to measure learning in authentic ways. This comprehensive strategy incorporates practical tools and processes to engage students, educators, and families in new partnerships and drive deep learning. Inside you’ll find: The Deep Learning Framework Vignettes and case studies from K-12 classrooms in 1,200 schools in seven countries Guidance for reaching disadvantaged and differently abled students Sample protocols and rubrics for assessment Videos demonstrating deep learning design and innovative leadership in practice Through learning partnerships, learning environments, new pedagogical practices, and leveraged digital skills, deep learning reaches students as never before — preparing them to be active, engaged participants in their future.

Objev podobné jako Deep Learning - Joanne Quinn, Michael Fullan, Joanne J. McEachen

Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based ... Unknown localization key: "more"

Objev podobné jako Programming Large Language Models with Azure Open AI - Francesco Esposito

This revised and updated textbook applies a critical and practical lens to the world of social media analytics. Author Jeremy Harris Lipschultz explores the foundations of digital data, strategic tools, and best practices in an accessible volume for students and practitioners of social media communication.This second edition expands upon entrepreneurship, marketing, and technological principles, demonstrating how raising awareness, sparking engagement, and producing business outcomes all require emphasis on customers, employees, and other stakeholders within paid, earned, social, and owned media. It also looks to the future, examining how the movement toward artificial intelligence and machine learning raises new legal and ethical issues in effective management of social media data. Additionally, the book offers a solid grounding in the principles of social media measurement itself, teaching the strategies and techniques that enable effective analysis. It features theoretical and practical advice, a comprehensive glossary of key terms, and case studies from academic and industry thought leaders.A perfect primer for this developing industry, this book is ideal for students, scholars, and practitioners of digital media seeking to hone their skills and expand their bank of new tools and resources.

Objev podobné jako Social Media Measurement and Management - Jeremy Harris Lipschultz

Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on ... Unknown localization key: "more"

Objev podobné jako SQL for Data Scientists - Renee M. P. Teate

Raise standards and improve learning for all students through equitable gradingGrading–one of the most important responsibilities of teachers with major implications for students’ academic and life trajectories–is ironically also among the most enigmatic and frequently avoided topics in education. Although most teachers sense that common grading practices are often ineffective, there is limited understanding of how those practices can undermine effective teaching and harm students, particularly those historically underserved. It is long past due to implement grading practices that are more accurate, bias-resistant, and motivational, and which improve student learning, empower teachers, and transform classrooms as a result.In this newly updated edition of the best-selling Grading for Equity, Joe Feldman provides a valuable resource for anyone invested in grading and its impact on students’ education, mental health, and future opportunities. Offering a research-based alternative to the status quo, this practitioner-friendly guide provides Extensive revisions that reflect how the pandemic and the Black Lives Matter movement shifted traditional grading systems New data from both academic research and classrooms that demonstrate the benefits of equitable grading for all students Clear approaches to implement equitable grading practices Updated information on several equitable grading practices, including proficiency scales A new concluding chapter that explores ... Unknown localization key: "more"

Objev podobné jako Grading for Equity - Joe Feldman

Written by bestselling author Ann Gravells, this is the complete go-to guide for anyone wanting to be (or working as) a teacher or trainer in the further education and skills sector, in the UK and beyond. It has all the information you need to work towards a qualification such as the Award, Certificate or Diploma in Education and Training. It is also relevant to anyone taking a Train the Trainer course, or an international teaching qualification.The book takes you through all the information you need to know, opening up the topic for learning in an easily accessible way. Interactive activities are included throughout, along with real examples of teaching and training in practice. The book also includes examples of completed teaching documents.This is a comprehensive text, covering: The role of a teacher/trainer Factors contributing to learning Planning and facilitating learning for groups and individuals Using technology and resources to support learning Assessing learning Quality assurance Evaluation, reflection, and continuing professional development (CPD)Â Preparing for a micro-teach session and teaching/observed practice Â

Objev podobné jako Principles and Practices of Teaching and Training - Ann Gravells

Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.

Objev podobné jako Smart Energy and Electric Power Systems

Scale has been the central promise of the digital turn. The creation of corpora such as EEBO and EEBO-TCP have eased the logistics of access to primary sources for scholars of Shakespeare and early English literature and culture and fundamentally altered the ways in which we retrieve, read, think about, and analyze texts. However, the large-scale curation of historical corpora poses unique challenges and requires scholarly insight and significant algorithmic intervention. In sectionschapters on ''Text,'' ''Corpus,'' ''Search,'' and ''Discovery,'' this Eelement problematizes the specific affordances of computation and scale as primary conceptual categories rather than incidental artifacts of digitization. From text-encoding and search to corpus-scale data visualization and machine-learning, it discusses a range of computational techniques that can facilitate corpus curation and enable exploratory, experimental modes of discovery that not only serve as tools to ease access but accommodate and respond to the demands of humanistic inquiry.

Objev podobné jako Shakespeare and Scale - Anupam Basu

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It''s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You''ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

Objev podobné jako Causal Inference for Data Science - Aleix de Villa

Covid-19 has shown us the importance of mathematical and statistical models to interpret reality, provide forecasts, and explore future scenarios.Algorithms, artificial neural networks, and machine learning help us discover the opportunities and pitfalls of a world governed by mathematics and artificial intelligence.

Objev podobné jako Algorithms for a New World - Alfio Quarteroni

Eager to learn AI and machine learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed.

Objev podobné jako AI and ML for Coders in Pytorch - Laurence Moroney

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

Objev podobné jako System Design for Epidemics Using Machine Learning and Deep Learning

Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you''ll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.

Objev podobné jako Ensemble Methods for Machine Learning - Gautam Kunapuli

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book''s web site.

Objev podobné jako Mathematics for Machine Learning - A. Aldo Faisal, Marc Peter Deisenroth, Cheng Soon Ong

Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging ''explore'' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.

Objev podobné jako Linear Algebra for Data Science, Machine Learning, and Signal Processing - Jeffrey A. Fessler, Raj Rao Nadakuditi

Written by more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets, and explores connections with data science and more traditional approaches. This is an invaluable resource for researchers and graduate students in financial engineering, as well as practitioners in the sector.

Objev podobné jako Machine Learning and Data Sciences for Financial Markets

It''s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis ... Unknown localization key: "more"

Objev podobné jako Grokking Machine Learning - Luis Serrano

Stable Diffusion, ChatGPT, Whisper - these are just a few examples of incredible applications powered by developments in machine learning. Despite the ubiquity of machine learning applications running in production, there are only a few viable language choices for data science and machine learning tasks. Elixir''s Nx project seeks to change that. With Nx, you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic language like Elixir. In this book, you''ll learn how to leverage Elixir and the Nx ecosystem to solve real-world problems in computer vision, natural language processing, and more.The Elixir Nx project aims to make machine learning possible without the need to leave Elixir for solutions in other languages. And even if concepts like linear models and logistic regression are new to you, you''ll be using them and much more to solve real-world problems in no time.Start with the basics of the Nx programming paradigm - how it differs from the Elixir programming style you''re used to and how it enables you to write machine learning algorithms. Use your understanding of this paradigm to implement foundational machine learning algorithms from scratch. Go deeper and discover the power of ... Unknown localization key: "more"

Objev podobné jako Machine Learning in Elixir - Sean Moriarity

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML ... Unknown localization key: "more"

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models.Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. •   Integrates coverage of cutting-edge research, existing tools, and real-world applications•   Provides students and professionals with an engineering view for production-ready machine learning systems•   Proven in the classroom•   Offers supplemental resources including slides, videos, ... Unknown localization key: "more"

Objev podobné jako Machine Learning in Production - Christian Kastner