Build Real AI Systems

By: Ajit Singh

Narrated by: Virtual Voice

"Build Real AI Systems" is a comprehensive, practical-first guide for students and aspiring developers aiming to master the end-to-end process of creating and deploying artificial intelligence applications. This book is engineered to serve as a direct, hands-on manual, emphasizing implementation over abstract theory. It is meticulously structured to align with the computer science curriculum of international universities, making it an ideal resource for B.Tech and M.Tech students, as well as for professionals seeking to acquire practical AI skills.


Philosophy

The core philosophy of this book is "Learning by Building." Traditional academic approaches often segregate theory and practice, leaving students with a solid theoretical understanding but little confidence in their ability to create a functional application from scratch. I reject this separation. Every concept introduced in this book is immediately tied to a practical purpose and demonstrated through a concrete implementation.
My guiding principle is to answer the question, "How do I build this?" at every stage. I believe that true understanding of an AI system comes not from memorizing formulas, but from wrestling with data, writing code, training models, debugging errors, and ultimately, deploying a service that works. This "builder's mindset" is at the heart of every chapter, guiding the reader from foundational setup to a fully realized capstone project.


Key Features

1. Strictly Practical Orientation: Over 80% of the content is dedicated to hands-on tutorials, code walkthroughs, and practical implementation details.

2. Modern Tooling: Utilizes the most relevant and widely-used tools in the AI industry, including Python, TensorFlow, Keras, Scikit-learn, Pandas, and frameworks for API deployment like Flask or FastAPI.

3. Deployment-Focused: A dedicated chapter on model deployment teaches how to wrap a trained model in an API and make it accessible over a network—a critical skill for any AI professional.

4. Complete Capstone Project: The final chapter guides the reader through building a complete, end-to-end AI application, including all source code and step-by-step instructions, reinforcing all the concepts learned throughout the book.

5. Simple Explanations: Core concepts are explained in the simplest possible terms, using analogies and practical examples to make them accessible to readers without a deep mathematical background.

6. University Syllabus Compatibility: The topics are carefully selected to cover the essential practical components of AI, Machine Learning, and Deep Learning courses found in B.Tech and M.Tech Computer Science programs worldwide.


Key Takeaways

Upon completing this book, you will be able to:

1. Set up and manage a professional Python-based AI development environment.

2. Implement the complete machine learning workflow, from data collection and cleaning to model evaluation.

3. Build, train, and fine-tune deep neural networks for tasks like image classification and text analysis using TensorFlow and Keras.

4. Apply powerful techniques like transfer learning to leverage state-of-the-art pre-trained models.

5. Develop practical applications in the domains of Computer Vision (CV) and Natural Language Processing (NLP).

6. Understand and implement the basics of Generative AI to create novel content.

7. Package a trained AI model into a web API for easy integration and deployment.

8. Confidently design and execute an end-to-end AI project, from initial concept to a deployed, functional application.

Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!