Journal of Next-Generation Research 5.0

Empowering the Future of Applied Artificial Intelligence

The AI Practical eBooks Collection is an innovative series designed to transform how students, engineers, researchers, and professionals learn and apply artificial intelligence. Unlike traditional academic resources, this collection delivers a fully hands-on experience, real-world-ready AI applications across a wide range of high-impact sectors including healthcare, insurance, supply chain, banking, energy, retail, education, agriculture, and more.

What Makes This Collection Different?

• Step-by-step implementations using structured, synthetic datasets
• Fully executable Python code with Jupyter/Colab integration or desktop Python environments
• Real-world-inspired case studies with downloadable, ready-to-run files
• No setup barriers: pre-configured environments for immediate exploration
• Practice-to-certification model: turn your work into a professional credential
• Tested, ready-to-deploy AI apps included in every book

From Learning to Recognition

This collection uniquely bridges learning and career validation. Each book walks you through the complete development of working AI applications. After completing a book, readers have the option to submit their deployment files—trained models, scripts, and fully functional apps—for expert review.

Submissions scoring 90 out of 100 or higher receive an official, individual certification (e.g., Certified AI Healthcare Modeler, AI Insurance Modeler) relevant to the domain of the book. A €250 certification fee applies and is available only to verified book owners.

All models can be built and deployed either in Google Colab or on a local desktop Python environment, especially for small datasets.

After working through the case studies using the provided synthetic data, readers can immediately apply the same pipelines to their own real-world projects. Since each book contains a ready-to-use AI application, users only need to replace the synthetic features and sample values with their actual features and data—everything else, from preprocessing to deployment, is already included and explained.

You learn by doing, then deploy by adapting—just replace the synthetic features with your real ones and launch your AI app in minutes.

Who Should Read This?

• Professionals applying AI in their sector (e.g., healthcare, banking, retail)
• Engineers & Data Scientists building real-world, operational pipelines
• Students & Researchers shifting from theory to application
• Organizations training talent for AI deployment
• Entrepreneurs developing intelligent, scalable solutions

Why We Use Synthetic Data (And Why That is a Strength)

Important Note: All models and case studies in this book are developed using synthetic datasets, carefully designed to simulate partially realistic patterns while avoiding any ethical, legal, or privacy issues. Sharing real-world data is often restricted—but learning how to build and evaluate AI systems does not have to be.

This approach enables readers to safely explore and apply full AI pipelines—including data preprocessing, model training, evaluation, deployment, and interpretation—without depending on inaccessible or sensitive datasets.

To move toward real-world deployment, we strongly recommend partnering with domain experts to source and validate actual datasets relevant to your field.

This book is your foundation. The path from simulation to production is yours to build—with the tools and knowledge you gain here.

How to Apply for Certification

To receive an official certification (e.g., Certified AI Healthcare Modeler, Certified AI Insurance Modeler), readers must complete all case studies in the book and submit their working deployment files for expert review.

Each book contains two case studies, and candidates are expected to:

• Select the best-performing model for each case study
• Include the complete, functional code covering:
  • Data preprocessing
  • Model training
  • Model saving and loading
  • Final deployment as an AI application
• Ensure that all code runs successfully and reflects the full, working pipeline

Accepted submission formats:

• .ipynb files (for Google Colab or Jupyter Notebook)
• .py files (for standard Python scripts)

Send your complete submission via the button below:

• Only submissions with complete and functioning code will be reviewed.
• A minimum score of 90 out of 100 is required to receive certification.
• The certification process is available only to verified book owners.
• The certification is granted only to individuals (not to companies) and is issued in partnership with a vocational academic center accredited by the Moroccan government.
• A €250 certification fee applies to the review and issuance process.

Corporate Training Requests

Organizations interested in training their teams to develop and deploy real-world AI applications can request a dedicated 3-day training program. This hands-on, project-based experience is designed and led by Dr. Rachid Ejjami and his expert partners to help employees build production-ready AI solutions tailored to their domain using proven methodology and tools.

All training sessions are based on the same fully executable AI pipelines featured in the eBook series. Teams will learn to adapt these pipelines to their internal datasets, without ever needing to share sensitive company data.

Training can be delivered in either English or French, depending on your team’s preference.

To request a custom training session for your company, please click the button below and fill out the request form: