Learn AI, Machine Learning, and Deep Learning in One Easy-to-Understand Guide

Artificial Intelligence is transforming the way we live, work, and interact—but understanding it doesn’t have to be overwhelming.

Artificial Intelligence Essentials You Always Wanted to Know is a clear, engaging, and practical introduction to AI—perfect for business professionals, career switchers, tech enthusiasts, and lifelong learners.

From understanding how AI models are built to exploring cutting-edge technologies like Neural Networks and Generative AI, each chapter breaks down complex ideas into manageable sections—complete with summaries and quizzes to reinforce key learnings.

Key Features of the Book

A Clear Introduction to AI

Learn how AI has evolved and how it’s transforming industries from finance and healthcare to retail and education.

Foundational Concepts Made Simple

Understand how AI solves problems, the three core learning paradigms, and the basics of model development.

Machine Learning Techniques Explained

Dive into key methods like regression, classification, and clustering, and discover when to apply them in real-world scenarios.

Deep Learning Demystified

Explore neural networks, CNNs, and RNNs, and understand how deep learning is driving innovation in various fields.

Explore Core AI Domains

• Natural Language Processing (NLP) – Learn how machines process and generate human language.
• Computer Vision (CV) – Discover how AI understands the visual world.
• Generative AI – Learn the basics of prompt engineering, large language models (LLMs), and content creation.

Bonus: Online Job Prep Support

Access exclusive interview questions to help you prepare for roles in a rapidly growing job market.

Take the first step toward mastering AI and equip yourself for the future that's already unfolding.

The Practical AI Playbook Every Manager Needs to Lead Smarter, Decide Faster, and Build Future-Ready Teams

Artificial intelligence is rapidly becoming part of everyday business operations. The managers who learn to use it effectively will be better equipped to improve productivity, adapt to change, and support high-performing teams.

AI Essentials for Managers provides a step-by-step roadmap for putting AI to work. Through practical examples and actionable strategies, readers learn how to leverage AI for communication, planning, collaboration, analysis, and decision-making—without needing a technical background.

Key Features Include:

• Practical AI applications for productivity, communication, planning, and decision-making
• Step-by-step guidance for introducing AI to teams without triggering resistance
• Ethical frameworks for responsible, people-centered AI adoption
• A Manager Prompt Library with ready-to-use prompts for common leadership tasks
• An AI Tools Cheat Sheet comparing popular tools by function, use case, and pricing

From drafting communications and summarizing lengthy documents to forecasting outcomes and making data-driven decisions, the book focuses on practical applications that deliver immediate value. Each chapter concludes with a "Try This Week" activity, helping readers turn insights into action right away. 

After reading this book, you will be able to:

• Apply AI tools to your most time-consuming management tasks
• Lead team adoption with a framework that addresses concerns before they escalate
• Develop responsible AI policies tailored to your organization
• Move from uncertainty to action with a clear 30-Day Plan

Part of Vibrant Publishers' Self-Learning Management Series, AI Essentials for Managers is the resource today's leaders have been waiting for.

• Covers key algorithms and techniques
• Ideal for students and professionals
• Hands-on implementation included

Master the fundamentals of ML and take the first step towards a career in AI!

In today’s rapidly evolving world, machine learning (ML) is no longer just for researchers or data scientists. From personalized recommendations on streaming platforms to fraud detection in banking, ML powers many aspects of our daily lives. As industries increasingly adopt AI-driven solutions, learning machine learning has become a valuable skill. Yet, many find the subject overwhelming, often intimidated by its mathematical complexity. That’s where Machine Learning Essentials You Always Wanted to Know (Machine Learning Essentials) comes in. This beginner-friendly guide offers a structured, step-by-step approach to understanding machine learning concepts without unnecessary jargon. Whether you are a student, a professional looking to transition into AI, or simply curious about how machines learn, this book provides a clear and practical roadmap to mastering ML.

Authored by Dhairya Parikh, an experienced data engineer who returned to academia to refine his expertise, this book bridges the gap between theory and real-world application. It simplifies the core concepts of ML, breaking them down into digestible explanations paired with hands-on coding exercises to help you apply what you learn.

What You’ll Learn:

• The fundamentals of machine learning and how it powers modern technology
• The three key types of ML—Supervised, Unsupervised, and Reinforcement Learning
• How to combine algorithms, data, and models to develop AI-driven solutions
• Practical coding techniques to build and implement machine learning models

Part of Vibrant Publishers’ Self-Learning Management Series, this book serves as a valuable guide for building machine learning skills, enhancing your expertise, and advancing your career in AI and data science.

What if the most important information in your data lies not in individual rows and columns, but in the connections between them? Graph machine learning helps uncover patterns hidden in these relationships.

Graph Machine Learning Essentials is a practical and accessible guide to understanding how machine learning works with graph-structured data, where entities are connected through relationships.

Designed for software engineers, ML engineers, data scientists, research scholars, professionals, cybersecurity analysts, and students, the book introduces graph machine learning in a clear and structured way. It begins with the fundamentals of graph theory and moves into core graph learning tasks such as node classification, edge prediction, and graph classification. Readers learn how graphs are represented in data structures, how node and edge embeddings work, and why traditional machine learning approaches do not directly apply to graph data.

The book gradually builds toward graph neural networks, message passing, and advanced GNN architectures while explaining practical challenges such as graph construction, scalability, oversmoothing, and over-squashing. Concepts are connected to real-world applications across domains such as recommender systems, fraud detection, cybersecurity, bioinformatics, transportation networks, and knowledge graphs.

The book includes two helpful appendices—one reviewing essential machine learning concepts and the other introducing PyTorch Geometric to help readers get started quickly.

After reading this book, you will be able to:

• Understand key graph machine learning concepts and terminology
• Implement graph neural networks using PyTorch Geometric
• Work on real-world graph learning problems across industries
• Handle practical challenges such as large graphs and oversmoothing