Ai For Data Science

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Analytical Skills for AI and Data Science

Daniel Vaughan

Author : Daniel Vaughan
Publisher : O'Reilly Media
Page : 244 pages
File Size : 43,6 Mb
Release : 2020-05-21
Category : Computers
ISBN : 9781492060918

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Analytical Skills for AI and Data Science by Daniel Vaughan Pdf

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. Break business decisions into stages that can be tackled using different skills from the analytical toolbox Identify and embrace uncertainty in decision making and protect against common human biases Customize optimal decisions to different customers using predictive and prescriptive methods and technologies Ask business questions that create high value through AI- and data-driven technologies

AI for Data Science

Zacharias Voulgaris,Yunus Emrah Bulut

Author : Zacharias Voulgaris,Yunus Emrah Bulut
Publisher : Unknown
Page : 0 pages
File Size : 42,9 Mb
Release : 2018
Category : Algorithms
ISBN : 1634624092

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AI for Data Science by Zacharias Voulgaris,Yunus Emrah Bulut Pdf

Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code. Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world. The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache's MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on. A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book's data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.

Data Analytics and AI

Jay Liebowitz

Author : Jay Liebowitz
Publisher : CRC Press
Page : 187 pages
File Size : 40,5 Mb
Release : 2020-08-06
Category : Computers
ISBN : 9781000094671

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Data Analytics and AI by Jay Liebowitz Pdf

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

Enabling AI Applications in Data Science

Aboul-Ella Hassanien,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa

Author : Aboul-Ella Hassanien,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa
Publisher : Springer Nature
Page : 644 pages
File Size : 53,6 Mb
Release : 2020-09-23
Category : Computers
ISBN : 9783030520670

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Enabling AI Applications in Data Science by Aboul-Ella Hassanien,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa Pdf

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.

AI & Data Literacy

Bill Schmarzo

Author : Bill Schmarzo
Publisher : Packt Publishing Ltd
Page : 239 pages
File Size : 47,6 Mb
Release : 2023-07-26
Category : Computers
ISBN : 9781835087947

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AI & Data Literacy by Bill Schmarzo Pdf

Learn the key skills and capabilities that empower Citizens of Data Science to not only survive but thrive in an AI-dominated world. Purchase of the print or Kindle book includes a free PDF eBook Key Features Prepare for a future dominated by AI and big data Enhance your AI and data literacy with real-world examples Learn how to leverage AI and data to address current and future challenges Book DescriptionAI is undoubtedly a game-changing tool with immense potential to improve human life. This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you’ll need to exploit to thrive amid the current and future developments in the AI landscape. We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions. Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed. Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and how best to leverage it and thrive alongside it.What you will learn Get to know the fundamentals of data literacy, privacy, and analytics Find out what makes AI tick and the role of the AI utility function Make informed decisions using prominent decision-making frameworks Understand relevant statistics and probability concepts Create new sources of value by leveraging and applying AI and data Apply ethical parameters to AI development with real-world examples Find out how to get the most out of ChatGPT and its peers Who this book is forThis book is designed to benefit everyone from students to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their AI and Data literacy.

Data Science on AWS

Chris Fregly,Antje Barth

Author : Chris Fregly,Antje Barth
Publisher : "O'Reilly Media, Inc."
Page : 524 pages
File Size : 46,7 Mb
Release : 2021-04-07
Category : Computers
ISBN : 9781492079361

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Data Science on AWS by Chris Fregly,Antje Barth Pdf

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value

Eric Anderson,Florian Zettelmeyer

Author : Eric Anderson,Florian Zettelmeyer
Publisher : McGraw Hill Professional
Page : 353 pages
File Size : 45,6 Mb
Release : 2020-11-23
Category : Business & Economics
ISBN : 9781260459159

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Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value by Eric Anderson,Florian Zettelmeyer Pdf

Lead your organization to become evidence-driven Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries. The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories. Inside, you’ll find the essential tools to help you: Develop a strong data science intuition quotient Lead and scale AI and analytics throughout your organization Move from “best-guess” decision making to evidence-based decisions Craft strategies and tactics to create real impact Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data.

Malware Data Science

Joshua Saxe,Hillary Sanders

Author : Joshua Saxe,Hillary Sanders
Publisher : No Starch Press
Page : 274 pages
File Size : 47,8 Mb
Release : 2018-09-25
Category : Computers
ISBN : 9781593278595

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Malware Data Science by Joshua Saxe,Hillary Sanders Pdf

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.

A Practical Guide to Artificial Intelligence and Data Analytics

Rayan Wali

Author : Rayan Wali
Publisher : Rayan Wali
Page : 605 pages
File Size : 40,9 Mb
Release : 2021-06-12
Category : Computers
ISBN : 8210379456XXX

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A Practical Guide to Artificial Intelligence and Data Analytics by Rayan Wali Pdf

Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics touches on all of the fundamental topics you will need to understand deeper into machine learning and artificial intelligence research, literature, and practical applications with its four parts: Part I: Concept Instruction Part II: 8 Full-Length Case Studies Part III: 50+ Mixed Exercises Part IV: A Full-Length Assessment With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design skills.

Machine Learning and Data Science Blueprints for Finance

Hariom Tatsat,Sahil Puri,Brad Lookabaugh

Author : Hariom Tatsat,Sahil Puri,Brad Lookabaugh
Publisher : "O'Reilly Media, Inc."
Page : 432 pages
File Size : 41,9 Mb
Release : 2020-10-01
Category : Computers
ISBN : 9781492073000

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Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat,Sahil Puri,Brad Lookabaugh Pdf

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Algorithmic Governance and Governance of Algorithms

Martin Ebers,Marta Cantero Gamito

Author : Martin Ebers,Marta Cantero Gamito
Publisher : Springer Nature
Page : 174 pages
File Size : 52,8 Mb
Release : 2020-10-08
Category : Law
ISBN : 9783030505592

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Algorithmic Governance and Governance of Algorithms by Martin Ebers,Marta Cantero Gamito Pdf

Algorithms are now widely employed to make decisions that have increasingly far-reaching impacts on individuals and society as a whole (“algorithmic governance”), which could potentially lead to manipulation, biases, censorship, social discrimination, violations of privacy, property rights, and more. This has sparked a global debate on how to regulate AI and robotics (“governance of algorithms”). This book discusses both of these key aspects: the impact of algorithms, and the possibilities for future regulation.

Artificial Intelligence, Machine Learning, and Data Science Technologies

Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry

Author : Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry
Publisher : CRC Press
Page : 311 pages
File Size : 42,6 Mb
Release : 2021-10-11
Category : Computers
ISBN : 9781000460520

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Artificial Intelligence, Machine Learning, and Data Science Technologies by Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry Pdf

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

Applying Data Science

Arthur K. Kordon

Author : Arthur K. Kordon
Publisher : Springer
Page : 494 pages
File Size : 51,7 Mb
Release : 2021-09-14
Category : Computers
ISBN : 3030363775

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Applying Data Science by Arthur K. Kordon Pdf

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.

An Introduction to Data

Francesco Corea

Author : Francesco Corea
Publisher : Springer
Page : 131 pages
File Size : 41,5 Mb
Release : 2018-11-27
Category : Technology & Engineering
ISBN : 9783030044688

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An Introduction to Data by Francesco Corea Pdf

This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.

Data Science and Machine Learning

Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman

Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Publisher : CRC Press
Page : 538 pages
File Size : 40,6 Mb
Release : 2019-11-20
Category : Business & Economics
ISBN : 9781000730777

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Data Science and Machine Learning by Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman Pdf

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Ai For Data Science

Analytical Skills for AI and Data Science by Daniel Vaughan Pdf

AI for Data Science by Zacharias Voulgaris,Yunus Emrah Bulut Pdf

Data Analytics and AI by Jay Liebowitz Pdf

Enabling AI Applications in Data Science by Aboul-Ella Hassanien,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa Pdf

AI & Data Literacy by Bill Schmarzo Pdf

Data Science on AWS by Chris Fregly,Antje Barth Pdf

Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value by Eric Anderson,Florian Zettelmeyer Pdf

Malware Data Science by Joshua Saxe,Hillary Sanders Pdf

A Practical Guide to Artificial Intelligence and Data Analytics by Rayan Wali Pdf

Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat,Sahil Puri,Brad Lookabaugh Pdf

Algorithmic Governance and Governance of Algorithms by Martin Ebers,Marta Cantero Gamito Pdf

Artificial Intelligence, Machine Learning, and Data Science Technologies by Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry Pdf

Applying Data Science by Arthur K. Kordon Pdf

An Introduction to Data by Francesco Corea Pdf

Data Science and Machine Learning by Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman Pdf

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