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

This book is a beginner-friendly guide to artificial intelligence (AI), ideal for those with no technical background. It introduces AI, machine learning, and deep learning basics, focusing on no-code methods for easy understanding. The book also covers data science, data mining, and big data processing, maintaining a no-code approach throughout. Practical applications are explored using no-code platforms like Microsoft Azure Machine Learning and AWS SageMaker. Readers are guided through step-by-step instructions and real-data examples to apply learning algorithms without coding. Additionally, it includes the integration of business intelligence tools like Power BI and AWS QuickSight into machine learning projects.This guide bridges the gap between AI theory and practice, making it a valuable resource for beginners in the field.

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.

Unlock the transformative power of Artificial Intelligence (AI) to propel your business to new heights. AI for Entrepreneurs is an essential guide for business owners looking to leverage AI technology to boost growth, optimize operations, and stay ahead of the competition. Packed with practical strategies, this book demystifies AI, making it accessible to entrepreneurs of all sizes—whether you're a startup founder or running a small enterprise. Discover how AI is revolutionizing industries by automating routine tasks, improving decision-making, and enhancing customer experiences. You'll learn step-by-step how to identify key areas where AI can add value, choose the right tools to enhance marketing and operations, and automate processes to save time and costs. Featuring real-world success stories of entrepreneurs who used AI to scale their businesses, this book will show you exactly how to implement AI in your daily operations for maximum impact. Bonus resources include a curated list of AI tools, an action plan template, and an easy-to-understand AI glossary—everything you need to start leveraging AI today. AI for Entrepreneurs is your roadmap to making AI a powerful ally in your business journey. Get your copy and start building your AI-powered success story now!

Machine learning and Artificial Intelligence are pillars on which you can build intelligent applications. This field is essential in the modern world since robots may now display complex cognitive abilities including as decision-making, learning and seeing the environment, behaviour prediction, and language processing. The terms "artificial intelligence" & "machine learning" are often used interchangeably, although they refer to two distinct processes. Machine learning is a branch of artificial intelligence that allows intelligent systems to autonomously learn new things from data, while artificial intelligence as a whole refers to robots that can make choices, acquire new skills, and solve problems. The engineering profession makes extensive use of AI methods to address a broad variety of previously intractable issues. The purpose of this book is to bring together developed form scientists, researchers, and academics to discuss all aspects of artificial intelligence and share their findings with one another and the wider scientific community. The book serves as a leading multidisciplinary forum for discussing real-world problems and the solutions that have been implemented to address them.

Machine learning (ML) is a subfield of AI that allows computers to "learn" from the data and improve over time without being explicitly programmed. Algorithms that use machine learning may analyze data for patterns and use that knowledge to generate predictions. To sum up, machine learning algorithms & models acquire knowledge from previous data. Traditional programming entails a computer engineer crafting a set of rules that tell a computer how to take raw data and produce a certain result. Most commands follow an IF-THEN format: the computer acts only if the specified condition holds. The opposite is true with machine learning, which is the automated process that allows computers to solve issues with little or no human intervention and to respond following what they have learned from previous experiences. The terms "artificial intelligence" & "machine learning" are often used interchangeably, although they refer to two distinct processes. Machine learning is a branch of artificial intelligence that allows intelligent systems to autonomously learn new things from data, while artificial intelligence as a whole refers to robots that can make choices, acquire new skills, and solve problems. You may train machine learning algorithms to conduct computations, process data, and recognize patterns without explicitly programming them to do so by providing them with samples of labeled data.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

The book is specially designed for beginners as well as experienced professional, where in the concepts are clearly explained with real time examples. It helps a beginner to easily understand the concepts and lso helps the experienced professionals with the practicalities provided.

TAGLINE The only MLOps guide you'll ever need KEY FEATURES ● Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. ● Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies. ● Develop advanced skills to implement scalable solutions by understanding the latest trends/tools and best practices. DESCRIPTION This book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives. WHAT WILL YOU LEARN ● Implement and manage end-to-end machine learning lifecycles. ● Utilize essential tools and technologies for MLOps effectively. ● Design and optimize data pipelines for efficient model training. ● Develop and train machine learning models with best practices. ● Deploy, monitor, and maintain models in production environments. ● Address scalability challenges and solutions in MLOps. ● Implement robust security practices to protect your ML systems. ● Ensure data governance, model compliance, and security in ML operations. ● Understand emerging trends in MLOps and stay ahead of the curve. WHO IS THIS BOOK FOR? This book is for data scientists, machine learning engineers, and data engineers aiming to master MLOps for effective model management in production. It’s also ideal for researchers and stakeholders seeking insights into how MLOps drives business strategy and scalability, as well as anyone with a basic grasp of Python and machine learning looking to enter the field of data science in production. TABLE OF CONTENTS 1. Introduction to MLOps 2. Understanding Machine Learning Lifecycle 3. Essential Tools and Technologies in MLOps 4. Data Pipelines and Management in MLOps 5. Model Development and Training 6. Model Optimization Techniques for Performance 7. Efficient Model Deployment and Monitoring Strategies 8. Scalability Challenges and Solutions in MLOps 9. Data, Model Governance, and Compliance in Production Environments 10. Security in Machine Learning Operations 11. Case Studies and Future Trends in MLOps Index