Machine Learning

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By Yada Pruksachatkun
In stock
$1408 $1268
Yada Pruksachatkun is a machine learning scientist at Infinitus, a conversational AI startup that automates calls in the healthcare system. She has worked on trustworthy natural language processing as an Applied Scientist at Amazon, and led the first healthcare NLP initiative within mid-sized startup ASAPP.  Matthew McAteer is the creator of 5cube Labs, an ML consultancy that has worked with over 100 companies in industries ranging from architecture to medicine to agriculture. Subhabrata (Subho) Majumdar is a Senior Applied Scientist at Splunk. Previously, he spent 3 years in AT&T, where he led research and development on ethical AI.
AuthorYada Pruksachatkun BindingPaperback
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By Daniel Situnayake
In stock
$2347 $2113
Daniel Situnayake is Head of Machine Learning at Edge Impulse, where he leads embedded machine learning R&D.  Jenny Plunkett is a Senior Developer Relations Engineer at Edge Impulse, where she is a technical speaker, developer evangelist, and technical content creator.
AuthorDaniel Situnayake BindingPaperback
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By Ai Publishing
In stock
$3000 $2100
How Is This Book Different? Most Python books assume you know how to code using Pandas, NumPy, and Matplotlib. But this book does not. The author spends a lot of time teaching you how actually write the simplest codes in Python to achieve machine learning models. In-depth coverage of the Scikit-learn library starts from the third chapter itself. Jumping straight to Scikit-learn makes it easy for you to follow along. The other advantage is  Jupyter Notebook  is used to write and explain the code right through this book. You can access the datasets used in this book easily by downloading them at runtime. You can also access them through the  Datasets  folder in the SharePoint and GitHub repositories. You also get to work on three hands-on mini-projects:
AuthorAi Publishing BindingPaperback
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By Davy Cielen
$4499 $3599
Davy Cielen  is one of the founders and managing partners of Optimately where he focuses on leading and developing data science projects and solutions in various sectors and closely follows new developments in data science. Before Optimately he worked on data science and big data projects at a major retailer. Arno Meysman  is one of the founders and managing partners of Optimately where he focuses on leading and developing data science projects and solutions in various sectors and closely follows new developments in data science. Before Optimately he worked on data science and big data projects at a major retailer. Apart from data science he is also into data visualisation and generally "Creating data-driven things that are smart, interactive and pretty". Mohamed Ali  is one of the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors.
AuthorDavy Cielen BindingPaperback
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By Jeff Smith
$4499 $4049
Jeff Smith  builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems.
AuthorJeff Smith BindingPaperback
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By Hefin I. Rhys
$4999 $3999
Hefin Ioan Rhys  is a senior laboratory research scientist in the Flow Cytometry Shared Technology Platform at The Francis Crick Institute. He spent the final year of his PhD program teaching basic R skills at the university. A data science and machine learning enthusiast, he has his own Youtube channel featuring screencast tutorials in R and R Studio.
AuthorHefin I. Rhys BindingPaperback
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By Jeff Carpenter, Patrick Mcfadin
In stock
$1526 $1373
Is Kubernetes ready for stateful workloads? This open source system has become the primary platform for deploying and managing cloud native applications. But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging. If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help.
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By Alexander Zai
In stock
$4999 $4499
About the book Deep Reinforcement Learning in Action  teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.
AuthorAlexander Zai BindingPaperback
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By Galit Shmueli
$14095 $11276
This product will be shipped on 28-02-2023
Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner  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.
AuthorGalit Shmueli BindingHardcover
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By Peter C. Bruce, Peter Gedeck, Galit Shmueli, Inbal Yahav Shen
$14095 $11276
This product will be shipped on 08-03-2023
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.
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By Ram Shankar Siva Kumar, Hyrum Anderson
$2795 $2236
This product will be shipped on 31-03-2023
In  Not With A Bug, But With A Sticker: Attacks on Machine Learning Systems and What To Do About Them , a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats. The authors take you on a sweeping tour – from inside secretive government organizations to academic workshops at ski chalets to Google’s cafeteria – recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.
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By Galit Shmueli, Peter C. Bruce, Kuber R. Deokar, Nitin R. Patel
$14095 $11276
This product will be shipped on 30-04-2023
Galit Shmueli, PhD,  is Distinguished Professor and Institute Director at National Tsing Hua University’s Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Peter C. Bruce,  is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. Kuber Deokar  is the Lead Instructional Operations Supervisor in Data Science at UpThink Experts, India. He is also a Faculty member at Statistics.com. Nitin R. Patel, PhD,  is cofounder and lead researcher at Cytel Inc. He was also a co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a visiting professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.
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