No Code Artificial Intelligence


No Code Artificial Intelligence
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No Code Artificial Intelligence


No Code Artificial Intelligence
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Author : Ambuj Agrawal
language : en
Publisher: BPB Publications
Release Date : 2023-03-07

No Code Artificial Intelligence written by Ambuj Agrawal and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-07 with Computers categories.


A practical guide that will help you build AI and ML solutions faster with fewer efforts and no programming knowledge KEY FEATURES ● Start your journey to become an AI expert today. ● Learn how to build AI solutions to solve complex problems in your organization. ● Get familiar with different No-code AI tools and platforms. DESCRIPTION “No-Code Artificial Intelligence” is a book that enables you to develop AI applications without any programming knowledge. Authored by the founder of AICromo (https://aicromo.com/), this book takes you through an array of examples that shows how to build AI solutions using No-code AI tools. The book starts by sharing insights on the evolution of No-code AI and the different types of No-code AI tools and platforms available in the market. The book then helps you start building applications of Machine Learning in Finance, Healthcare, Sales, and Cybersecurity. It will also teach you to create AI applications to perform sales forecasting, find fraudulent claims, and detect diseases in plants. Furthermore, the book will show how to build Machine Learning models for a variety of use cases in image recognition, video object recognition, and data prediction. After reading this book, you will be able to build AI applications with ease. WHAT YOU WILL LEARN ● Use different No-code AI tools such as AWS Sagemaker, DataRobot, and Google AutoML. ● Learn how to create a Machine Learning model to predict housing prices. ● Build Natural Language Processing (NLP) models for Healthcare information Identification. ● Learn how to build an AI model to create targeted customer offerings. ● Use traditional ways to perform AI implementation using programming languages and AI libraries. WHO THIS BOOK IS FOR This book is for anyone who wants to build an AI app without writing any code. It is also helpful for current and aspiring AI and Machine Learning professionals who are looking to build automated, intelligent, and smart AI-based solutions. TABLE OF CONTENTS 1. What is AI? 2. Getting Started with No-Code AI 3. Building AI Model to Predict Housing Prices 4. Classifying Different Images 5. Building AI Model to Perform Sales Forecasting 6. Building AI Model to Find Fraudulent Claims 7. Building AI Model to Detect Diseases in Plants 8. Building AI Model to Create Targeted Customer Offerings 9. Building AI Model for Healthcare Information Identification 10. Building AI Model for Video Action Recognition 11. Building AI Applications with Coded AI



The No Code Guide To Artificial Intelligence And Machine Learning


The No Code Guide To Artificial Intelligence And Machine Learning
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Author : Hyun-Jeong Yoo
language : en
Publisher:
Release Date : 2018-03-22

The No Code Guide To Artificial Intelligence And Machine Learning written by Hyun-Jeong Yoo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-22 with categories.


Okay so AI has been in the news lately. Some rich tech billionaire says AI (or deep learning?) is going to take all the jobs and we are going to be poor on the streets or something. Or maybe you read some article by some dude that AI is in fact a dumb thing. This book is designed to teach you a very specific thing. Its goal is to get you to one place.This book wants you to do just one thing: Know just enough AI so that you can talk about it in a business meeting without a triggering a BS detector. You will not learn how to program AI or deep learning or machine learning or whatever here. There is no real reason to learn it - more on why I say that in the book. You are not going to spend pages reading about how AI will ruin our lives as we know it. For the most part, the stuff does not really work. More on why in the book. We are going to talk about AI in the real world - the issues that creators and users of the products experience everyday. Learn about what gets people to tear out their hair or shake their heads in frustration. By reading this book, you can expect to get: * A crash course that at least tells you the differences between AI, machine learning, deep learning and neural networks. * A rundown of today's AI applications from speech recognition to self-driving cars * An overview of possible AI-centric revenue models * A discussion of various issues in AI business entrepreneurship * An analysis on the process of deep learning product development including accuracy improvement, knowledge transfer and designWritten out of experiences spent working in business development for an AI company, this book dives into the AI and how it affects the corporate world. It is meant to be accessible and engaging, the right way to take your first steps into a new world. Download a sample and check it out.



Artificial Intelligence For Developers In Easy Steps


Artificial Intelligence For Developers In Easy Steps
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Author : Richard Urwin
language : en
Publisher: In Easy Steps
Release Date : 2024-07-30

Artificial Intelligence For Developers In Easy Steps written by Richard Urwin and has been published by In Easy Steps this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with categories.


Artificial Intelligence for Developers in easy steps is for anyone who wants to learn more about Artificial Intelligence (AI) and build AI applications using basic programming and No-Code AI tools. It has an easy-to-follow style that will be suited to anyone who wants to get started with AI programming. It is aimed at beginners but is also helpful for current and aspiring AI and Machine Learning professionals who are looking to build intelligent AI solutions: Enthusiasts who want to understand and learn more about AI technologies. Web developers wanting to add AI features to their web applications. Programmers wanting to add AI programming to their skillset. Students or those seeking a career in computing.



No Code Data Science


No Code Data Science
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Author : David Patrishkoff
language : en
Publisher:
Release Date : 2023-10-16

No Code Data Science written by David Patrishkoff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-16 with Computers categories.


No Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first-of-its-kind textbook book is designed to empower readers with the ability to leverage advanced analytics, machine learning, and AI without using a programming language, such as Python or R.It's a comprehensive guide to no-code data science (NCDS) that applies free, no-code, and open-source software with Orange visual programming software, JASP, and BlueSky Statistics. A no-shortcuts approach to ML and AI is applied to maximize the accuracy and application potential of predictive models. The NCDS approach is akin to constructing predictive models with pre-made LEGO bricks (visual programming) versus tediously molding shapes from clay (manual coding). A practical how-to approach to predictive modeling is offered while insisting on the rigor of our disciplined NCDS process. Hands-on data exercises are included in the first eleven chapters. QR code links to educational videos are included in most chapters.Data science background is first explored, discussing basic definitions and data scientist skill sets. This is followed by chapters on data preparation, wrangling, and data visualization. Predictive analytics is covered in chapters on machine learning models and model evaluation. Both supervised and unsupervised learning are included in the discourse. Time series forecasting, survival analysis, and geolocation are covered in separate chapters. Artificial intelligence is featured in chapters on image analysis and text mining. Lastly, the potential impact of machine learning and artificial intelligence on Industry 4.0 is covered in the last chapter. A pathway for statisticians, Lean Six Sigma practitioners, and other professionals is offered to learn predictive modeling techniques to enable organizations to successfully pursue Industry 4.0 goals.



No Code Data Science


No Code Data Science
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Author : David Patrishkoff
language : en
Publisher:
Release Date : 2023-10-16

No Code Data Science written by David Patrishkoff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-16 with Computers categories.


No-Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first-of-its-kind textbook book is designed to empower readers with the ability to leverage advanced analytics, machine learning, and AI without using a programming language, such as Python or R.It's a comprehensive guide to no-code data science (NCDS) that applies free, no-code, and open-source software with Orange visual programming software, JASP, and BlueSky Statistics. A no-shortcuts approach to ML and AI is applied to maximize the accuracy and application potential of predictive models. The NCDS approach is akin to constructing predictive models with pre-made LEGO bricks (visual programming) versus tediously molding shapes from clay (manual coding). A practical how-to approach to predictive modeling is offered while insisting on the rigor of our disciplined NCDS process. Hands-on data exercises are included in the first eleven chapters. QR code links to educational videos are included in most chapters.Data science background is first explored, discussing basic definitions and data scientist skill sets. This is followed by chapters on data preparation, wrangling, and data visualization. Predictive analytics is covered in chapters on machine learning models and model evaluation. Both supervised and unsupervised learning are included in the discourse. Time series forecasting, survival analysis, and geolocation are covered in separate chapters. Artificial intelligence is featured in chapters on image analysis and text mining. Lastly, the potential impact of machine learning and artificial intelligence on Industry 4.0 is covered in the last chapter. We also offer a pathway for statisticians, Lean Six Sigma practitioners, and other professionals to learn predictive modeling techniques to enable organizations to successfully pursue Industry 4.0 goals.



Low Code Ai


Low Code Ai
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Author : Gwendolyn Stripling
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-09-13

Low Code Ai written by Gwendolyn Stripling and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-13 with Computers categories.


Take a data-first and use-case–driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish between structured and unstructured data and the challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different ML model types and architectures, from no code to low code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance



No Code Required


No Code Required
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Author : Allen Cypher
language : en
Publisher: Morgan Kaufmann
Release Date : 2010-05-21

No Code Required written by Allen Cypher and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-21 with Computers categories.


No Code Required presents the various design, system architectures, research methodologies, and evaluation strategies that are used by end users programming on the Web. It also presents the tools that will allow users to participate in the creation of their own Web. Comprised of seven parts, the book provides basic information about the field of end-user programming. Part 1 points out that the Firefox browser is one of the differentiating factors considered for end-user programming on the Web. Part 2 discusses the automation and customization of the Web. Part 3 covers the different approaches to proposing a specialized platform for creating a new Web browser. Part 4 discusses three systems that focus on the customized tools that will be used by the end users in exploring large amounts of data on the Web. Part 5 explains the role of natural language in the end-user programming systems. Part 6 provides an overview of the assumptions on the accessibility of the Web site owners of the Web content. Lastly, Part 7 offers the idea of the Web-active end user, an individual who is seeking new technologies. The first book since Web 2.0 that covers the latest research, development, and systems emerging from HCI research labs on end user programming tools Featuring contributions from the creators of Adobe’s Zoetrope and Intel’s Mash Maker, discussing test results, implementation, feedback, and ways forward in this booming area



Low Code Ai


Low Code Ai
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Author : Gwendolyn Stripling
language : en
Publisher: O'Reilly Media
Release Date : 2023-10-31

Low Code Ai written by Gwendolyn Stripling and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-31 with Computers categories.


Take a data-first and use-case driven approach to understanding machine learning and deep learning concepts with Low-Code AI. This hands-on guide presents three problem-focused ways to learn ML: no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data, feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish structured and unstructured data and understand the different challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different machine learning model types and architectures, from no code to low-code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance



Machine Learning For Kids


Machine Learning For Kids
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Author : Dale Lane
language : en
Publisher: No Starch Press
Release Date : 2021-02-09

Machine Learning For Kids written by Dale Lane and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Computers categories.


A hands-on, application-based introduction to machine learning and artificial intelligence (AI). Create compelling AI-powered games and applications using the Scratch programming language. AI Made Easy with 13 Projects Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based companion website, you’ll see how easy it is to add machine learning to your own projects. You don’t even need to know how to code! Step by easy step, you’ll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve them. You’ll turn your models into 13 fun computer games and apps, including: A Rock, Paper, Scissors game that recognizes your hand shapes A computer character that reacts to insults and compliments An interactive virtual assistant (like Siri or Alexa) A movie recommendation app An AI version of Pac-Man There’s no experience required and step-by-step instructions make sure that anyone can follow along! No Experience Necessary! Ages 12+



The No Code Startup


The No Code Startup
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Author : Emma Reilly
language : en
Publisher: Practical Inspiration Publishing
Release Date : 2024-02-26

The No Code Startup written by Emma Reilly and has been published by Practical Inspiration Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Business & Economics categories.


Have you ever dreamed of developing the next big app like Airbnb or TikTok, but you don’t know your webhooks from your APIs? Do you have a software idea that could solve the world’s biggest problems but coding seems like a dark art? What was once a heavy barrier to most founders - creating a technical product - has now become simple and accessible thanks to the world of No-Code. The No-Code Startup is a play-by-play guide to launching your business by building just about any kind of app with No-Code tools. You’ll learn the basics of storing data, building automations and even implementing AI tools like GPT. By the end you will be able to test your product with real customers before continuing your journey as a true tech startup founder.