Machine Learning with Dynamics 365 and Power Platform: The Ultimate Guide to Learning and Applying Machine Learning and Predictive Analytics, Bansal Vinnie, Clere Aurelien
Описание: Cambridge for DGB 2nd Edition is a four-level American English integrated skills series for the Upper Secondary public school market in Mexico. Its syllabus is strictly aligned to the national Direccion General del Bachillerato program. It is a series that offers teachers a hands-on and practical solution to teaching English in the classroom. It builds students` language skills from A1 to A2+ in the CEFR.
Автор: Valentine Fontama; Roger Barga; Wee Hyong Tok Название: Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition ISBN: 1484212010 ISBN-13(EAN): 9781484212011 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.
Want to predict what your customers want to buy without them having to tell you? Want to accurately forecast sales trends for your marketing team better than any employee could ever do? Then keep reading.
You've heard it before. The rise of artificial intelligence and how it will soon replace human beings and take away our jobs. What exactly is it capable of and how does this impact me? The real question you should be asking yourself is how can I use this to my advantage? How can I use machine learning to benefit my business and surpass my business goals? This book has the answer.
Designed for the tech novice, this book will break down the fundamentals of machine learning and what it truly means. You will learn to leverage neural networks, predictive modelling, and data mining algorithms, illustrated with real-world applications for finance, business and marketing.
Machine learning isn't just for scientists or engineers anymore. It's become accessible to anyone, and you can discover it's benefits for your business.
In Machine Learning for Beginners 2019, we will reveal:
✅ The fundamentals of machine learning.
✅ Each of the buzzwords defined
✅ 20 real-world applications of machine learning.
✅ How to predict when a customer is about to churn (and prevent it from happening).
✅ How to "upsell" to your customers and close more sales.
✅ How to deal with missing data or poor data.
✅ Where to find free datasets and libraries.
✅ Exactly which machine learning libraries you need.
✅ And much much more
I know you might be overwhelmed at this point, but I assure you this book has been designed for absolute beginners. Everything is in plain English. There is no code, so no coding experience is required. You won't walk away a machine learning god, but you will walk away with key strategies you can implement right away to improve your business.
���� If you are ready to start making big changes to your business, scroll up and click buy. ����
Автор: Satpathy Rabinarayan, Choudhury Tanupriya, Satpathy Suneeta Название: Data Analytics in Bioinformatics: A Machine Learning Perspective ISBN: 1119785537 ISBN-13(EAN): 9781119785538 Издательство: Wiley Рейтинг: Цена: 28979.00 р. Наличие на складе: Поставка под заказ.
Описание: Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Intelligent machines are revolutionizing business.
Machine learning and data analytics are powering a wave of groundbreaking technologies. Is your company ready?
If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them.
In this book you'll learn how:
Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights
Blockchain has the potential to restructure the economy
Drones and driverless vehicles are becoming essential tools
3-D printing is making new business models possible
Augmented reality is transforming retail and manufacturing
Smart speakers are redefining the rules of marketing
Humans and machines are working together to reach new levels of productivity
This collection of articles includes "Artificial Intelligence for the Real World," by Thomas H. Davenport and Rajeev Ronanki; "Stitch Fix's CEO on Selling Personal Style to the Mass Market," by Katrina Lake; "Algorithms Need Managers, Too," by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; "Marketing in the Age of Alexa," by Niraj Dawar; "Why Every Organization Needs an Augmented Reality Strategy," by Michael E. Porter and James E. Heppelmann; "Drones Go to Work," by Chris Anderson; "The Truth About Blockchain," by Marco Iansiti and Karim R. Lakhani; "The 3-D Printing Playbook," by Richard A. D'Aveni; "Collaborative Intelligence: Humans and AI Are Joining Forces," by H. James Wilson and Paul R. Daugherty; "When Your Boss Wears Metal Pants," by Walter Frick; and "Managing Our Hub Economy," by Marco Iansiti and Karim R. Lakhani.
Описание: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Описание: This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.
Do you want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model, and you have no programming skills? Do you want to get started with learning data science?
This book is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This book is going to help you understand the various methods of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
Machine learning implies programming. This book will teach you Python programming. This book does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning.
Overall, this book is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the book, you will be able to develop your machine learning models using Python.
Through this book, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to build and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models.
Автор: Kalita Название: Recent Developments in Machine Learning and Data Analytics ISBN: 9811312796 ISBN-13(EAN): 9789811312793 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents high-quality papers from an international forum for research on computational approaches to learning. Further, it features work that shows how to apply learning methods to solve important application problems as well as how machine learning research is conducted.
Автор: Ulf Brefeld; Jesse Davis; Jan Van Haaren; Albrecht Название: Machine Learning and Data Mining for Sports Analytics ISBN: 3030172732 ISBN-13(EAN): 9783030172732 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018.
The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge papers are included, reporting on how to predict pass receivers in soccer.
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