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.
Contents:

[*]Introduction to Artificial Intelligence:   
[*]Overview of Artificial Intelligence
[*]Machine Learning
[*]Model Validation and Evaluation
[*]Data and Source Collection for Machine Learning
[*]Deep Learning
[*]Case Study

[*]Platform-Based Artificial Intelligence:   
[*]Amazon Web Service (AWS)
[*]Amazon SageMaker
[*]SageMaker Canvas
[*]SageMaker Canvas Practice
[*]SageMaker Studio
[*]SageMaker Autopilot
[*]Microsoft Azure
[*]Azure Automated Machine Learning
[*]Azure Pipeline

[*]BI Solutions:   
[*]Amazon QuickSight
[*]Utilizing QuickSight
[*]Power BI
[*]Power BI Desktop Tutorial

Readership: Advanced undergraduate and graduate students or artificial intelligence and machine learning; researchers, and practitioners in the fields of IT, AI; and individuals new to AI.
Key [b]Features:[/b]

[*]Accessible to Beginners: Specifically designed for individuals new to information technology (IT) and artificial intelligence (AI), making complex concepts understandable without requiring prior programming knowledge
[*]Broad Audience Appeal: Appeals to a wide range of readers, from those interested in the mathematical foundations of AI to seasoned engineers looking for alternatives to open-source solutions
[*]Step-by-Step Tutorials: Contains detailed, easy-to-follow tutorials on Microsoft Azure Machine Learning, allowing readers to start from scratch and build functional machine learning models
[*]Comprehensive Coverage: Not only focuses on Azure Machine Learning but also introduces readers to AWS SageMaker, providing a comparative insight into two of the leading cloud-based machine learning platforms
[*]Integration with Data Analytics Tools: Explores the use of Power BI and AWS QuickSight for data visualization, highlighting how these tools can enhance machine learning projects by making results actionable and insightful
[*]Real-World Applications: Features practical projects and case studies using public and medical datasets, demonstrating the real-world applicability of the tools and techniques discussed
[*]Alternative to Open Source: Addresses the challenges of using open-source software for AI and machine learning, presenting Azure Machine Learning and AWS SageMaker as viable, user-friendly alternatives
[*]Future-Oriented Discussion: Speculates on the future developments in AI platforms, preparing readers for upcoming trends and technologies in the field of AI and machine learning
[*]Resource Guide for Continued Learning: Offers an extensive list of resources, including online courses, forums, and documentation, to assist readers in furthering their understanding and skills after finishing the book
[*]Empowerment Through Knowledge: Empowers readers by equipping them with the knowledge and tools needed to apply artificial intelligence in various domains, regardless of their IT background