A Practical Guide To Artificial Intelligence And Data Analytics
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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.
A Practical Guide to AI 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 three parts: Part I: A Conceptual (and Visual) Illustration [topics including, but not limited to, are listed below] Fundamentals of Data Science The Data and Machine Learning Pipelines Data Preprocessing + Worked Data Preprocessing Strategy Data Visualization Python for Data Analysis Calculus & Linear Algebra Fundamentals Data Structures and Algorithms Exercises Machine Learning Models & Algorithms (kNN, Neural Networks, Hidden Markov Models, Ensemble Methods, etc.) Deep Learning for Computer Vision & NLP (CNNs, RNNs, etc.) Data Mining Model Deployment Time Series Data Analysis AI Systems in the Real-World Applications of Data Analysis Exercises Database Systems & Cloud Computing (with practical example) Functional Programming for Data Analytics Part II: 10 Full-Length Case Studies Case Study I: Sports Web Scraping Case Study II: NLP Textual Analysis Case Study III: Emergency Response Duration Analysis Case Study IV: MNIST Image Classification Case Study V: COVID-19 Statistical Data Analysis Case Study VI: COVID-19 Chest X-Ray Screening Case Study VII: Signal Strength Geospatial Analysis Case Study VIII: NYC Crash Accidents Data Analysis Case Study IX: Sales Forecasting Case Study X: Meteorite Landings Analysis Part III: A Full-Length Data Science and Analytics Skills Assessment (DSSA) With exercises that span a wide range of AI problems from different domains, from the economics and finance to transportation and medical industries, the DSSA aims to provide a comprehensive assessment to measure your understanding through cleverly-designed AI reasoning, problem-solving, and scenario-based exercises, whether you use it to enhance your understanding in the AI and Data Analytics field or use it to prepare for your AI/Data Analytics problem solving and system design interviews. Section I: 60 Multiple-Choice and Short-Answer Exercises Section II: 5 AI & Data Analytics Problem Solving and Coding Exercises Solutions to Sections I and II are included 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.
Intelligent Systems for Engineers and Scientists by Adrian A. Hopgood Pdf
The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/
Learn Microsoft Fabric by Arshad Ali,Bradley Schacht Pdf
Harness the power of Microsoft Fabric to develop data analytics solutions for various use cases guided by step-by-step instructions Key Features Explore Microsoft Fabric and its features through real-world examples Build data analytics solutions for lakehouses, data warehouses, real-time analytics, and data science Monitor, manage, and administer your Fabric platform and analytics system to ensure flexibility, performance, security, and control Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, and the wider analytics landscape. In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you’ll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey. By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.What you will learn Get acquainted with the different services available in Microsoft Fabric Build end-to-end data analytics solution to scale and manage high performance Integrate data from different types of data sources Apply transformation with Spark, Notebook, and T-SQL Understand and implement real-time stream processing and data science capabilities Perform end-to-end processes for building data analytics solutions in the AI era Drive insights by leveraging Power BI for reporting and visualization Improve productivity with AI assistance and Copilot integration Who this book is for This book is for data professionals, including data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers who want to build a future-ready data analytics solution for long-term success in the AI era. For PySpark and SQL students entering the data analytics field, this book offers a broad foundation for developing the skills to build end-to-end analytics systems for various use cases. Basic knowledge of SQL and Spark is assumed.
The Ultimate Guide to Data Science and Analytics This practical guide is accessible for the reader who is relatively new to the field of data analytics, while still remaining robust and detailed enough to function as a helpful guide to those already experienced in the field. Data science is expanding in breadth and growing rapidly in importance as technology rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater. RIGHT NOW you can get ahead of the pack! This coherent guide covers everything you need to know on the subject of data science, with numerous concrete examples, and invites the reader to dive further into this exciting field. Students from a variety of academic backgrounds, including computer science, business, engineering, statistics, anyone interested in discovering new ideas and insights derived from data can use this as a textbook. At the same time, professionals such as managers, executives, professors, analysts, doctors, developers, computer scientists, accountants, and others can use this book to make a quantum leap in their knowledge of big data in a matter of only a few hours. Learn how to understand this field and uncover actionable insights from data through analytics. UNDERSTAND the following key insights when you grab your copy today: WHY DATA IS IMPORTANT TO YOUR BUSINESS DATA SOURCES HOW DATA CAN IMPROVE YOUR BUSINESS HOW BIG DATA CREATES VALUE DEVELOPMENT OF BIG DATA CONSIDERING THE PROS AND CONS OF BIG DATA BIG DATA FOR SMALL BUSINESSES THE COST EFFECTIVENESS OF DATA ANALYTICS WHAT TO CONSIDER WHEN PREPARING FOR A NEW BIG DATA SOLUTION DATA GATHERING DATA SCRUBBING DESCRIPTIVE ANALYTICS INFERENTIAL STATISTICS PREDICTIVE ANALYTICS PREDICTIVE MODELS DESCRIPTIVE MODELING DECISION MODELING PREDICTIVE ANALYSIS METHODS MACHINE LEARNING TECHNIQUES DATA ANALYSIS WITH "R" ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT (CRM) THE USE OF PREDICTIVE ANALYTICS IN HEALTHCARE THE USE OF PREDICTIVE ANALYTICS IN THE FINANCIAL SECTOR PREDICTIVE ANALYTICS & BUSINESS MARKETING STRATEGIES FRAUD DETECTION SHIPPING BUSINESS CONTROLLING RISK FACTORS THE REVOLUTION OF PREDICTIVE ANALYSIS ACROSS A VARIETY OF INDUSTRIES DESCRIPTIVE AND PREDICTIVE ANALYSIS CRUCIAL FACTORS FOR DATA ANALYSIS RESOURCES AND FLEXIBLE TECHNICAL STRUCTURE BUSINESS INTELLIGENCE HYPER TARGETING WHAT IS DATA SCIENCE? DATA MUNGING DEMYSTIFYING DATA SCIENCE SECURITY RISKS TODAY BIG DATA AND IMPACTS ON EVERYDAY LIFE FINANCE AND BIG DATA APPLYING SENTIMENT ANALYSIS RISK EVALUATION AND THE DATA SCIENTIST THE FINANCE INDUSTRY AND REAL-TIME ANALYTICS HOW BIG DATA IS BENEFICIAL TO THE CUSTOMER CUSTOMER SEGMENTATION IS GOOD FOR BUSINESS USE OF BIG DATA BENEFITS IN MARKETING GOOGLE TRENDS THE PROFILE OF A PERFECT CUSTOMER LEAD SCORING IN PREDICTIVE ANALYSIS EVALUATING THE WORTH OF LIFETIME VALUE BIG DATA ADVANTAGES AND DISADVANTAGES MAKING COMPARISONS WITH COMPETITORS DATA SCIENCE IN THE TRAVEL SECTOR SAFETY ENHANCEMENTS THANKS TO BIG DATA BIG DATA AND AGRICULTURE BIG DATA AND LAW ENFORCEMENT THE USE OF BIG DATA IN THE PUBLIC SECTOR BIG DATA AND GAMING PRESCRIPTIVE ANALYTICS GOOGLE'S "SELF-DRIVING CAR" AND MUCH MORE! WANT MORE? Scroll up and grab this helpful guide toady!
Artificial Intelligence for Business by Rajendra Akerkar Pdf
This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.
Artificial Intelligence and Machine Learning for Business by Steven Finlay Pdf
Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Organizations which understand these tools and know how to use them are benefiting at the expense of their rivals. Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is very much on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies. This third edition has been substantially revised and updated. It contains several new chapters and covers a broader set of topics than before, but retains the no-nonsense style of the original.
Making Sense of Data I by Glenn J. Myatt,Wayne P. Johnson Pdf
Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.
Practical Machine Learning for Data Analysis Using Python by Abdulhamit Subasi Pdf
Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Practical Data Analysis by Hector Cuesta,Dr. Sampath Kumar Pdf
A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.
Driving Digital Transformation through Data and AI by Alexander Borek,Nadine Prill Pdf
Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.
The Secrets of AI Value Creation by Michael Proksch,Nisha Paliwal,Wilhelm Bielert Pdf
Unlock unprecedented levels of value at your firm by implementing artificial intelligence In The Secrets of AI Value Creation: Practical Guide to Business Value Creation with Artificial Intelligence from Strategy to Execution, a team of renowned artificial intelligence leaders and experts delivers an insightful blueprint for unlocking the value of AI in your company. This book presents a comprehensive framework that can be applied to your organisation, exploring the value drivers and challenges you might face throughout your AI journey. You will uncover effective strategies and tactics utilised by successful artificial intelligence (AI) achievers to propel business growth. In the book, you’ll explore critical value drivers and key capabilities that will determine the success or failure of your company’s AI initiatives. The authors examine the subject from multiple perspectives, including business, technology, data, algorithmics, and psychology. Organized into four parts and fourteen insightful chapters, the book includes: Concrete examples and real-world case studies illustrating the practical impact of the ideas discussed within Best practices used and common challenges encountered when first incorporating AI into your company’s operations A comprehensive framework you can use to navigate the complexities of AI implementation and value creation An indispensable blueprint for artificial intelligence implementation at your organisation, The Secrets of AI Value Creation is a can’t-miss resource for managers, executives, directors, entrepreneurs, founders, data analysts, and business- and tech-side professionals looking for ways to unlock new forms of value in their company. The authors, who are industry leaders, assemble the puzzle pieces into a comprehensive framework for AI value creation: Michael Proksch is an expert on the subject of AI strategy and value creation. He worked with various Fortune 2000 organisations and focuses on optimising business operations building customised AI solutions, and driving organisational adoption of AI through the creation of value and trust. Nisha Paliwal is a senior technology executive. She is known for her expertise in various technology services, focusing on the importance of bringing AI technology, computing resources, data, and talent together in a synchronous and organic way. Wilhelm Bielert is a seasoned senior executive with an extensive of experience in digital transformation, program and project management, and corporate restructuring. With a proven track record, he has successfully led transformative initiatives in multinational corporations, specialising in harnessing the power of AI and other cutting-edge technologies to drive substantial value creation.
The Analytics Lifecycle Toolkit by Gregory S. Nelson Pdf
An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
Artificial Intelligence and Legal Analytics by Kevin D. Ashley Pdf
This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.
Hands-On Artificial Intelligence for Banking by Jeffrey Ng,Subhash Shah Pdf
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers in KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via Open Bank APISense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.
