392 results on 'LN cat08778a'
Search Results
2. Clean Ruby: A Guide to Crafting Better Code for Rubyists#
- Author
-
Carleton DiLeo
- Subjects
Internet of things ,Machine learning ,Electronic books - Published
- 2020
3. Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets.
- Author
-
Ciaburro, Giuseppe and Joshi, Prateek
- Subjects
Python (Computer program language) ,Machine learning ,Electronic books - Published
- 2019
4. Hands-on supervised machine learning with Python. [electronic resource]
- Subjects
Python (Computer program language) ,Machine learning ,Instructional films - Abstract
Summary: Teach your machine to think for itself! About This Video: Take a deep dive into supervised learning and grasp how a machine "learns" from data. Follow detailed and thorough coding examples to implement popular machine learning algorithms from scratch, developing a deep understanding along the way. Work your Python muscle! This course will help you grow as a developer by heavily relying on some of the most popular scientific and mathematical libraries in the Python language. In Detail: Supervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, while allowing the system to self-adjust and make decisions on its own. This makes it crucial to know how a machine "learns" under the hood.This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning. Next, we'll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning. By the end of the video course, you'll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems. All the codes of the course are uploaded on GitHub.
- Published
- 2018
5. Business analytics.
- Author
-
Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
6. Business analytics.
- Author
-
Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
7. Fundamentals of deep learning : designing next-generation machine intelligence algorithms.
- Author
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Buduma, Nikhil and Locascio, Nicholas
- Subjects
Artificial intelligence ,Machine learning ,Neural networks (Computer science) ,Deep learning ,Künstliche Intelligenz ,Maschinelles Lernen ,Electronic books - Published
- 2017
8. A First Course in Machine Learning.
- Author
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Rogers, Simon and Girolami, Mark
- Subjects
Machine learning ,COMPUTERS -- General ,Data Mining ,Maschinelles Lernen ,Machine Learning ,Electronic books - Published
- 2016
9. Learning Spark : lightening fast data analysis.
- Author
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Karau, Holden
- Subjects
ApacheSpark ,Big data ,Machine learning ,Electronic books - Abstract
Summary: This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.
- Published
- 2015
10. Kernel methods and machine learning.
- Author
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Kung, S. Y.
- Subjects
Support vector machines ,Machine learning ,Kernel functions ,COMPUTERS / Computer Vision & Pattern Recognition - Abstract
Summary: "Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors"-- Provided by publisher.
- Published
- 2014
11. Machine learning in action.
- Author
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Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Engineering & Applied Sciences ,Computer Science ,Electronic book ,Electronic books ,Handbooks and manuals - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
12. Machine learning in action.
- Author
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Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Engineering & Applied Sciences ,Computer Science ,Electronic book ,Electronic books ,Handbooks and manuals - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
13. Ensembles in machine learning applications.
- Author
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Okun, Oleg, Valentini, Giorgio, and Re, Matteo
- Subjects
Machine learning ,Set theory - Published
- 2011
14. Deep learning for physical scientists : accelerating research with machine learning.
- Author
-
Pyzer-Knapp, Edward O. and Benatan, Matthew
- Subjects
Physical sciences -- Data processing ,Machine learning - Abstract
Summary: "The rise of data-driven technologies such as machine learning has had wide ranging impacts, not least in the realm of physical sciences, where it is transforming the traditional mind-set about how research can, and should, be performed. Deep learning is an exciting new development in the area of machine learning, containing many powerful techniques which can benefit researchers in the physical sciences"-- Provided by publisher.
- Published
- 2022
15. Probabilistic machine learning : an introduction.
- Author
-
Murphy, Kevin P.
- Subjects
Machine learning ,Probabilities ,Linear model - Abstract
Summary: "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- Provided by publisher.
- Published
- 2022
16. Empowering artificial intelligence through machine learning : new advances and applications.
- Author
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Raju, Nedunchezhian, Rajalakshmi, M., Goyal, Dinesh, Balamurugan, S., Prof, Elngar, Ahmed A., and Keswani, Bright
- Subjects
Machine learning ,Artificial intelligence -- Industrial applications - Abstract
Summary: "This new volume, Empowering Artificial Intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next big-game changer in research and technology. The volume looks at how computing has enabled machines to learn, making machines and tools become smarter in many sectors, including science and engineering, healthcare, finance, education, gaming, security, and even agriculture, plus many more areas. Topics include techniques and methods in artificial intelligence for making machines intelligent, machine learning in healthcare, using machine learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes mapping, and much more. The book will be valuable for professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology"-- Provided by publisher.
- Published
- 2022
17. Big data, IoT, and machine learning : tools and applications.
- Author
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Agrawal, Rashmi, Paprzycki, Marcin, and Gupta, Neha
- Subjects
Big data ,Internet of things ,Machine learning - Abstract
Summary: The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases
- Published
- 2021
18. Machine Learning Design Patterns: solutions to common challenges in data preparation, model building, and MLOps.
- Author
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Lakshmanan, Valliappa, Robinson, Sara, and Munn, Michael
- Subjects
Machine learning ,Big data ,Design patterns - Abstract
Summary: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure that models are treating users fairly.
- Published
- 2021
19. Machine learning.
- Author
-
Alpaydin, Ethem
- Subjects
Machine learning ,Artificial intelligence - Abstract
Summary: "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Provided by publisher.
- Published
- 2021
20. The law of artificial intelligence.
- Author
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Hervey, Matt and Lavy, Matthew
- Subjects
Artificial intelligence ,Machine learning - Abstract
Summary: The Law of Artificial Intelligence is an essential practitioner's reference text examining how key areas of current civil and criminal law will apply to AI and examining emerging laws specific to the use of AI. It explains the fundamentals of AI technology, its development and terminology. The book also covers regulation, ethics and the use of AI within legal services and the administration of justice.
- Published
- 2021
21. Grokking machine learning.
- Author
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Serrano, Luis G.
- Subjects
Machine learning - Published
- 2021
22. Data Science on AWS : Implementing End to end, Continuous AI and Machine Learning Pipeline.
- Author
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Fregly, Chris and Barth, Antje
- Subjects
Machine Learning ,Cloud computing ,American Web services - Abstract
Summary: If you use data to make critical business decisions, this book is for you. Whether you're a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale. The AWS data science stack unifies data science, data engineering, and application development to help you level up your skills beyond your current role. Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs, submit them to the cloud, and integrate results into your application in minutes instead of days. Innovate quickly and save money with AWS's on-demand, serverless, and cloud-managed services Implement open source technologies such as Kubeflow, Kubernetes, TensorFlow, and Apache Spark on AWS Build and deploy an end-to-end, continuous ML pipeline with the AWS data science stack Perform advanced analytics on at-rest and streaming data with AWS and Spark Integrate streaming data into your ML pipeline for continuous delivery of ML models using AWS and Apache Kafka.
- Published
- 2021
23. Artificial intelligence and deep learning in pathology.
- Author
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Cohen, Stanley
- Subjects
Pathology -- Data processing ,Artificial intelligence -- Medical applications ,Machine learning ,Pathology ,Medical Informatics - Abstract
Summary: Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, with a team of experts, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience.
- Published
- 2021
24. Handbook of research on disease prediction through data analytics and machine learning.
- Author
-
Rani, Geeta and Tiwari, Pradeep Kumar
- Subjects
Machine Learning ,Diagnosis, Computer-Assisted ,Fuzzy Logic ,Data Interpretation, Statistical ,Sampling Studies - Abstract
Summary: "This book explores the use of data analytics algorithms and machine learning techniques for disease prediction"-- Provided by publisher.
- Published
- 2021
25. Art in the age of machine learning.
- Author
-
Audry, Sofian
- Subjects
Computer art ,Art and computers ,Machine learning - Abstract
Summary: "This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"-- Provided by publisher.
- Published
- 2021
26. Demystifying big data, machine learning, and deep learning for healthcare analytics / edited by Pradeep Nijalingappa, Sandeep Kautish, Sheng Lung Peng.
- Author
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Nijalingappa, Pradeep, Kautish, Sandeep, and Peng, Sheng Lung
- Subjects
Medical informatics ,Machine learning ,Big data - Abstract
Summary: "Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians"-- Provided by publisher.
- Published
- 2021
27. Agriculture 5.0 : artificial intelligence, IOT and machine learning.
- Author
-
Ahmad, Latief and Nabi, Firasath
- Subjects
Artificial intelligence--Agriculture applications ,Technology agriculture ,Machine learning - Abstract
Summary: "Agriculture 5.0: Artificial Intelligence, IoT & Machine Learning provides an interdisciplinary, integrative overview of latest development in the domain of smart farming. It shows how the traditional farming practices are being enhanced and modified by automation and introduction of modern scalable technological solutions that cut down on risks, enhance sustainability, and deliver predictive decisions to the grower, in order to make agriculture more productive. An elaborative approach has been used to highlight the applicability and adoption of key technologies and techniques such WSN, IoT, AI and ML in agronomic activities ranging from collection of information, analysing and drawing meaningful insights from the information which is more accurate, timely and reliable.It synthesizes interdisciplinary theory, concepts, definitions, models and findings involved in complex global sustainability problem-solving, making it an essential guide and reference. It includes real-world examples and applications making the book accessible to a broader interdisciplinary readership. This book clarifies hoe the birth of smart and intelligent agriculture is being nurtured and driven by the deployment of tiny sensors or AI/ML enabled UAV's or low powered Internet of Things setups for the sensing, monitoring, collection, processing and storing of the information over the cloud platforms. This book is ideal for researchers, academics, post-graduate students and practitioners of agricultural universities, who want to embrace new agricultural technologies for Determination of site-specific crop requirements, future farming strategies related to controlling of chemical sprays, yield, price assessments with the help of AI/ML driven intelligent decision support systems and use of agri-robots for sowing and harvesting. The book will be covering and exploring the applications and some case studies of each technology, that have heavily made impact as grand successes. The main aim of the book is to give the readers immense insights into the impact and scope of WSN, IoT, AI and ML in the growth of intelligent digital farming and Agriculture revolution 5.0.The book also focuses on feasibility of precision farming and the problems faced during adoption of precision farming techniques, its potential in India and various policy measures taken all over the world. The reader can find a description of different decision support tools like crop simulation models, their types, and application in PA. Features: Detailed description of the latest tools and technologies available for the Agriculture 5.0. Elaborative information for different type of hardware, platforms and machine learning techniques for use in smart farming. Elucidates various types of predictive modeling techniques available for intelligent and accurate agricultural decision making from real time collected information for site specific precision farming. Information about different type of regulations and policies made by all over the world for the motivation farmers and innovators to invest and adopt the AI and ML enabled tools and farming systems for sustainable production"-- Provided by publisher.
- Published
- 2021
28. Applied Machine Learning.
- Author
-
Forsyth, David
- Subjects
Neural Networks ,Artificial intelligence ,Machine Learning ,Probability and statistics - Abstract
Summary: Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it’s worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.
- Published
- 2021
29. Practical machine learning for computer vision : end-to-end machine learning for images.
- Author
-
Lakshmanan, Valliappa, Görner, Martin, and Gillard, Ryan
- Subjects
Computer vision ,Machine learning ,End to end machine learning - Abstract
Summary: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data pre-processing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.
- Published
- 2021
30. Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies.
- Author
-
Kelleher, John D., Mac Namee, Brian, and D'Arcy, Aoife
- Subjects
Machine learning ,Data mining ,Prediction theory - Abstract
Summary: "Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals."--
- Published
- 2020
31. Machine learning in finance : from theory to practice.
- Author
-
Dixon, Matthew F., Halperin, Igor, and Bilokon, Paul A.
- Subjects
Finance -- Data processing ,Machine learning ,Machine learning in Finance - Abstract
Summary: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
- Published
- 2020
32. Machine learning and data science blueprints for finance : from building trading strategies to robo-advisors using Python.
- Author
-
Tatsat, Hariom, Puri, Sahil, and Lookabaugh, Brad
- Subjects
Finance -- Data processing ,Finance -- Mathematical models ,Machine learning ,Natural language processing (Computer science) ,Python (Computer program language) - Abstract
Summary: Machine learning and data science will significantly transform the finance industry in the next few years. With this practical guide, professionals at hedge funds, investment and retail banks, and fintech firms will learn how to build ML algorithms crucial to this industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).
- Published
- 2020
33. Machine learning and its applications.
- Author
-
Wlodarczak, Peter
- Subjects
Machine learning ,Data mining ,Data analysis - Abstract
Summary: "This book describes Machine Learning techniques and algorithms that have been used in recent real-world application. It provides an introduction to Machine Learning, describes the most widely used techniques and methods. It also covers Deep Learning and related areas such as function approximation or. The book gives real world examples where Machine Learning techniques are applied and describes the basic math and the commonly used learning techniques"-- Provided by publisher.
- Published
- 2020
34. Machine learning for asset management : new trends and challenges.
- Author
-
Jurczenko, Emmanuel
- Subjects
Financial Applications ,Machine learning ,Investments--Data processing ,Assets management - Abstract
Summary: This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
- Published
- 2020
35. Mastering machine learning on AWS : advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow.
- Author
-
Mengle, Saket S.R and Gurmendez, Maximo
- Subjects
Machine learning ,Data mining ,AWS cloud - Abstract
Summary: AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis.
- Published
- 2020
36. Learn data mining through Excel. [electronic resource] : a step-by-step approach for understanding machine learning methods.
- Author
-
Zhou, Hong
- Subjects
Data mining ,Machine learning ,Microsoft Software - Abstract
Summary: Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn: Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel This book is for anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended. Hong Zhou, PhD is a professor of computer science and mathematics and has been teaching c ourses in computer science, data science, mathematics, and informatics at the University of Saint Joseph for more than 15 years. His research interests include bioinformatics, data mining, software agents, and blockchain. Prior to his current position, he was as a Java developer in Silicon Valley. Dr. Zhou believes that learners can develop a better foundation of data mining models when they visually experience them step-by-step, which is what Excel offers. He has employed Excel in teaching data mining and finds it an effective approach for both data mining learners and educators.
- Published
- 2020
37. Machine learning with R : expert techniques for predictive modeling.
- Author
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Lantz, Brett
- Subjects
R Programming ,Machine Learning ,Computer Science ,Expert Techniques - Abstract
Summary: A hands-on, readable guide to machine learning with R. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights and make new predictions. The 3rd edition features newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning.
- Published
- 2020
38. Machine learning applications in non-conventional machining processes.
- Author
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Bose, Goutam Kumar and Pain, Pritam
- Subjects
Machining -- Data processing ,Machine learning - Abstract
Summary: "This book is a collection of research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field"-- Provided by publisher.
- Published
- 2020
39. Machine learning in cognitive IoT.
- Author
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Kumar, Neeraj and Makkar, Aaisha
- Subjects
Embedded computer systems ,Machine learning ,Internet of things - Abstract
Summary: This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions
- Published
- 2020
40. Deep learning technologies and applications.
- Author
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Prudhomme, Gerard
- Subjects
Deep learning ,Machine learning ,Meta Learning - Abstract
Summary: Deep learning tools could very well end up with a substantially higher standard of accuracy and reliability in the recognition of physical objects, in some instances more advanced than individual overall human performance. Deep learning is an outlet of understanding, or perhaps studying, that takes advantage of a number of levels of non-linear processor jobs to discover how you can make representations of highly effective daily processes unswervingly from computer data.The first chapter refers to deep learning. Chapter 2 shows that when provided with genomic variance computer data from a variety of people, calculating the chance of complicated populace hereditary designs can often be improbable. Chapter 3 looks at how live-cell imaging provides you with started out a thrilling range into the function cellular heterogeneity performs in vibrant, subsistence devices.Chapter 4 looks at how protein contacts provide you with crucial information and facts for the comprehension of protein frameworks. Chapter 5 suggests a structure for foretelling updates in electronic community end user behavior. Chapter 6 looks at how precise computational recognition of promoters continues to be an issue.Chapter 7 shows that an innovative intrusion detection system ( IDS ) making use of a deep neural network ( DNN ) is offered to improve the safety of in-vehicular system. Chapter 8 looks at how event identification is easily the most basic and also crucial job in event-based all-natural vocabulary processing devices. Chapter 9 looks at getting a grasp on the cell-specific merging designs of transcription factors.Chapter 10 looks at what exactly is the source of our capability to understand orthographic information. Chapter 11 displays comprehending the simplest way to understand blockage at one area.
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- 2020
41. Deep learning : algorithms and applications.
- Author
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Pedrycz, Witold and Chen, Shyi-Ming
- Subjects
Machine learning ,Computer algorithms - Abstract
Summary: This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigms algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
- Published
- 2020
42. Machine Learning for Time Series Forecasting With Python.
- Author
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Lazzeri, Francesca
- Subjects
Machine Learning ,Python (Computer program language ,Time series forecasting - Abstract
Summary: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
- Published
- 2020
43. Probabilistic machine learning for civil engineers.
- Author
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Goulet, James-A
- Subjects
Machine learning ,Probabilities ,Machine Learning--Civil engineers - Abstract
Summary: "The book introduces probabilistic machine learning concepts to civil engineering students and professionals, who typically do not have the background necessary to understand the subject from a purely computer science perspective. It presents key approaches among the three sub-fields of machine learning: supervised, unsupervised, and reinforcement learning. The methods are demonstrated through step-by-step examples and copius illustrations in order to simplify abstract concepts. The book will prepare readers to access the vast body of literature from the field of machine learning"-- Provided by publisher.
- Published
- 2020
44. Clean Ruby: A Guide to Crafting Better Code for Rubyists#
- Author
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DiLeo, Carleton
- Subjects
Internet of things ,Ruby ,Computer programming ,Machine learning - Abstract
Summary: Learn how to make better decisions and write cleaner Ruby code. This book shows you how to avoid messy code that is hard to test and which cripples productivity. Author Carleton DiLeo shares hard-learned lessons gained from years of experience across numerous codebases both large and small. Each chapter covers the topics you need to know to make better decisions and optimize your productivity. Many books will tell you how to do something; this book will tell you why you should do it. Start writing code you love. What You Will Learn Build better classes to help promote code reuse Improve your decision making and make better, smarter choices Identify bad code and fixed it Create quality names for all of your variables, classes, and modules Write better, concise classes Improve the quality of your methods Properly use modules Clarify your Boolean logic See when and how you refactor Improve your understanding of TDD and write better tests Who This Book Is For This book is written for Ruby developers. There is no need to learn a new language or translate concepts to Ruby.
- Published
- 2020
45. Applied Machine Learning for Health and Fitness : A Practical Guide to Machine Learning with Deep Vision, Sensors and IoT.
- Author
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Ashley, Kevin
- Subjects
Machine learning ,Exercise ,Data processing - Abstract
Summary: Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley--who happens to be both a machine learning expert and a professional ski instructor--has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the authors practical expertise in both tech and sports is an undeniable asset for your learning process. Todays data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. You will: Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science.
- Published
- 2020
46. Computational intelligence for machine learning and healthcare informatics.
- Author
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Srivastava, Rajshree, Mallick, Pradeep Kumar, Rautaray, Siddharth Swarup, and Pandey, Manjusha
- Subjects
Artificial intelligence -- Medical applications ,Machine learning ,Machine Learning ,Medical Informatics -- methods - Published
- 2020
47. Reservoir simulations : machine learning and modeling.
- Author
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Sun, Shuyu and Zhang, Tao
- Subjects
Machine learning ,Computer simulation - Abstract
Summary: Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today's petroleum and reservoir engineer to optimize more complex developments.
- Published
- 2020
48. Artificial Intelligence by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills.
- Author
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Rothman, Denis
- Subjects
Artificial intelligence ,Machine learning ,Google Translator ,Computer Algorithms - Abstract
Summary: Artificial Intelligence (AI) gets your system to think smart and learn intelligently. This book is packed with some of the smartest trending examples with which you will learn the fundamentals of AI. By the end, you will have acquired the basics of AI by practically applying the examples in this book.
- Published
- 2020
49. Machine learning approaches to non-intrusive load monitoring.
- Author
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Bonfigli, Roberto and Squartini, Stefano
- Subjects
Machine learning ,Hidden markov model ,Deep neural network - Abstract
Summary: Research on Smart Grids has recently focused on the energy monitoring issue, with the objective of maximizing the user consumption awareness in building contexts on the one hand, and providing utilities with a detailed description of customer habits on the other. In particular, Non-Intrusive Load Monitoring (NILM), the subject of this book, represents one of the hottest topics in Smart Grid applications. NILM refers to those techniques aimed at decomposing the consumption-aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. This book provides a status report on the most promising NILM methods, with an overview of the publically available dataset on which the algorithm and experiments are based. Of the proposed methods, those based on the Hidden Markov Model (HMM) and the Deep Neural Network (DNN) are the best performing and most interesting from the future improvement point of view. One method from each category has been selected and the performance improvements achieved are described. Comparisons are made between the two reference techniques, and pros and cons are considered. In addition, performance improvements can be achieved when the reactive power component is exploited in addition to the active power consumption trace.
- Published
- 2020
50. Machine learning for healthcare : handling and managing data.
- Author
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Agrawal, Rashmi, Chatterjee, Jyotir Moy, Kumar, Abhishek, Rathore, Pramod Singh, and Le, Dac-Nhuong
- Subjects
Machine Learning ,Bio informatics ,Computers--Machine theory - Abstract
Summary: "This book will provide in depth information about handling and managing healthcare data by Machine Learning methods. It will express the long-standing challenges in healthcare informatics and provide rational orientations on how to deal with them"-- Provided by publisher.
- Published
- 2020
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