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5 Ways Companies Use Machine Learning to Improve Workplace Productivity

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Technology has become so advanced that, today, there's an app for almost anything, from children's education, to home improvement, to health monitoring, to workplace productivity. Gathering critical data to determine the best action to apply to specific situations has become integral in people's daily lives. Because of technology, critical decisions are now mostly based on scientific data. This makes every action more precise and error-free, especially in the business world. By using artificial intelligence and machine learning, industries can better cope with their consumers' demands.


Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA: Malik, Alok, Tuckfield, Bradford: 9781789956399: Amazon.com: Books

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Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA [Malik, Alok, Tuckfield, Bradford] on Amazon.com. *FREE* shipping on qualifying offers. Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA


Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science (Studies in Computational Intelligence, 975): Jin, Yaochu, Wang, Handing, Sun, Chaoli: 9783030746391: Amazon.com: Books

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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.


Machine Learning: Zhou, Zhi-Hua, Liu, Shaowu: 9789811519666: Amazon.com: Books

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Zhi-Hua Zhou is a leading expert on machine learning and artificial intelligence. He is currently a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and the founding director of the LAMDA Group at Nanjing University, China. Prof. Zhou has authored the books "Ensemble Methods: Foundations and Algorithms" (2012) and "Machine Learning" (in Chinese, 2016), and published more than 200 papers in top-tier international journals and conferences. He founded the ACML (Asian Conference on Machine Learning), and served as chairperson for many prestigious conferences, including AAAI 2019 program chair, ICDM 2016 general chair, IJCAI 2015 machine learning track chair, and area chair for NeurIPS, ICML, AAAI, IJCAI, KDD, etc. He is editor-in-chief of Frontiers of Computer Science, and has been an associate editor for prestigious journals such as the Machine Learning journal and IEEE PAMI.


The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition: Bateman, Blaine, Jha, Ashish Ranjan, Johnston, Benjamin, Mathur, Ishita: 9781800209046: Amazon.com: Books

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He graduated w/Special Honors in ChE & later Cert. in Quality Mgmt. Syndicated research (silicon photonics); writes for trade press and web communities. Served Fortune 1000 and FTSE 250 companies in a variety of projects, including global market/product strategy and most recently deep analytics and forecasting. Following ten years in government research and management (Deputy Director, National Measurement Laboratory (US DoC NIST) and Chief, Chemical Engineering Division of NIST), Mr. Bateman worked at several start-ups in electronics and antennas, resulting in 100s of products and several patents. Mr. Bateman led efforts to bring design and manufacturing of telematics and in-building antennas to China and Malaysia, and was key in creating an Automotive Connectivity Unit in Laird, and led technical diligence for multiple acquisitions and creation of an Infrastructure Antenna Unit.


The Kaggle Book: Data analysis and machine learning for competitive data science: Banachewicz, Konrad, Massaron, Luca, Goldbloom, Anthony: 9781801817479: Amazon.com: Books

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You can find lots of information on Kaggle about competing, but it is difficult to know what is relevant and also very expensive in terms of time and effort – so we put all the essential knowledge into one book. Konrad: My favorite part is Chapter 12 on simulation competitions. Reinforcement learning is a field I have been getting into over the last few years – unlike computer vision or NLP, it has yet to reach wider appeal outside academic circles. It was an interesting and educational experience to try and distill what I have learned into a useful introduction to that fascinating domain. Luca: I enjoyed writing about the history of Kaggle and the professional opportunities it offers.


Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375): Kivinen, Jyrki, Sloan, Robert H.: 9783540438366: Amazon.com: Books

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Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375) [Kivinen, Jyrki, Sloan, Robert H.] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings (Lecture Notes in Computer Science, 2375)


Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics: Nield, Thomas: 9781098102937: Amazon.com: Books

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I will make the argument that the disciplines of math and statistics have captured mainstream interest because of the growing availability of data, and we need math, statistics, and machine learning to make sense of it. Yes, we do have scientific tools, machine learning, and other automations that call to us like sirens. We blindly trust these "black boxes," devices, and softwares; we do not understand them but we use them anyway. While it is easy to believe computers are smarter than we are (and this idea is frequently marketed), the reality cannot be more the opposite. This disconnect can be precarious on so many levels.


An Introduction to Computational Learning Theory (The MIT Press): Kearns, Michael J., Vazirani, Umesh: 9780262111935: Amazon.com: Books

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Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.


Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press): Minsky, Marvin, Papert, Seymour A., Bottou, Leon: 9780262534772: Amazon.com: Books

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Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press) [Minsky, Marvin, Papert, Seymour A., Bottou, Leon] on Amazon.com. *FREE* shipping on qualifying offers. Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press)