A Practical Guide To Ai And Data Analytics

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.

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.

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.

Understand Data Analytics and Implement it in Your Business Today Do you want improve your revenue and stop missing out on profit? Do you want to learn about how data analytics in a style and approach that is suitable for you, regardless of your current knowledge? This book not only provides step-by-step guide to data analytics, but teaches you actionable steps to improve your analysis in all environments! Are you ready to learn? If so, Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert(Data Analytics, Prescriptive Analytics, Statistics, Big Data, Intelligence, Master Data, Data Science, Data Mining)by James Fahl is THE book for you! It covers the most essential topics you must learn to become a master of Data Analytics. What Separates This Book From The Rest? What separates this book from the rest? The unique way you will learn with examples and steps. Many books leave you more confused than before you picked them up, not this book, it's clear concise and implementable. We make it our goal to write this book in plain easy to understand English that anyone can understand. Gone are the days of highly technical language. This allows you to quickly learn topics, and use your new skills immediately. To aid you in learning the topics quickly and effectively this book has been designed to be the ultimate step-by-step guide. Making sure that you're confident and clear with each topic before moving on! You Will Learn The Following: What is Data Analytics? Why use Data Analytics The importance of Data Analytics Types of Data Analytics Explanations of Different models Collecting Data Mistakes to avoid Whether you just want to learn more about Data Analysis or already know but want a step-by-step guide to implement it in your life, this is the book for you! So don't delay it any longer. Take this opportunity and invest in your self by buying this guide now. You will be shocked by how fast you learn about Data Analytics! Don't Delay And Scroll Up To Buy With 1 Click

This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.

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.

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.

This book provides managers and decision-makers with the tools to make more informed decisions about big data purchasing initiatives. It not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market. Comparing and contrasting the different types of analysis commonly conducted wi