Bundles

AI bundle for professionals who want AI working alongside them, not just answering questions

Explore Large Language Models and AI Agents through diagrams, visual explanations and brief concepts covering transformers, RAG, MCP, memory and enterprise AI.

Start your journey from data analysis to advanced artificial intelligence. Python for Artificial Intelligence combines three practical books that guide you through data analytics, machine learning, and deep learning using Python and industry-standard tools. Learn. Build. Predict. Innovate.

From your first LLM call to production-grade AI systems. Seven books covering agents, MCP, prompt engineering, evals, cost optimization, and observability — the complete engineering stack for building with AI in 2026. Save over 40% versus buying individually.

Everything is connected.People, computers, businesses, cities, knowledge, and intelligent systems all exist within networks of relationships.Understanding those relationships is the key to modern Artificial Intelligence.The Graph Theory with AI Applications Complete Series (VOL-1 & VOL-2) takes readers from classical graph theory foundations to cutting-edge Graph Neural Networks and Graph AI.Inside this bundle, you'll discover:✓ Graph Theory Fundamentals✓ Graph Traversal and Pathfinding Algorithms✓ Shortest Path and Network Optimization✓ Social Network Analysis✓ Community Detection and Graph Mining✓ Graph Representation Learning✓ Graph Embeddings (DeepWalk, Node2Vec, LINE)✓ Graph Neural Networks (GCN, GAT, GraphSAGE)✓ Knowledge Graphs and Graph-Based NLP✓ AI-Powered Recommendation Systems✓ Cybersecurity and Fraud Detection✓ Explainable Graph AI✓ Distributed Graph Learning✓ Graph Foundation Models✓ Quantum Graph Neural NetworksWhether you are a student, researcher, educator, or AI professional, this bundle provides the mathematical foundations and modern AI techniques required to understand how intelligent systems learn from connected data.Learn Graph Theory.Master Graph Neural Networks.Build the future of connected intelligence.

What is intelligence?At its core, intelligence is the ability to acquire, compress, transform, communicate, and generate information.The Information Theory and Artificial Intelligence Complete Series explores the mathematical foundations that power modern machine learning, deep learning, generative AI, reinforcement learning, and large language models.Inside this two-volume series, you'll discover:✓ Shannon Entropy and Information Measures✓ Mutual Information and Representation Learning✓ Cross-Entropy and KL Divergence✓ Data Compression and Coding Theory✓ Information Bottleneck Theory✓ Variational Autoencoders (VAEs)✓ Contrastive and Self-Supervised Learning✓ Generative Adversarial Networks (GANs)✓ Entropy-Driven Reinforcement Learning✓ Fisher Information and Information Geometry✓ Information Theory Behind Transformers and LLMs✓ Federated Learning and Distributed Intelligence✓ Explainable AI Through Entropy✓ Quantum Information Theory✓ AI Fairness, Safety, Alignment, and Future ResearchWhether you are learning information theory for the first time or exploring the mathematical foundations of advanced AI systems, this bundle provides the tools, theory, and insights needed to understand how information becomes intelligence.Learn not just how AI works—but why it works.

What happens when intelligent machines must compete, cooperate, negotiate, and learn in the same environment?The answer lies at the intersection of Game Theory and Artificial Intelligence.This complete two-volume series explores how modern AI systems make strategic decisions in environments filled with uncertainty, competition, cooperation, and dynamic interactions.Inside the bundle, you'll discover:✓ Classical Game Theory and Nash Equilibrium✓ Multi-Agent Systems and Distributed Intelligence✓ Reinforcement Learning and Multi-Agent RL✓ Deep Reinforcement Learning Algorithms✓ Nash Q-Learning, Minimax-Q, and Correlated-Q✓ DQN, Actor-Critic, MADDPG, QMIX, and CTDE✓ Mechanism Design and Incentive Engineering✓ Decision Making Under Uncertainty✓ Social Choice Theory and Collective Intelligence✓ Applications in Robotics, Economics, Networks, and Cybersecurity✓ Evolutionary and Quantum Game Theory✓ AI Safety, Governance, and Alignment✓ Future Research Directions in Strategic AIFrom autonomous vehicles and intelligent robots to cybersecurity defense systems and large-scale AI simulations, this bundle provides the mathematical foundations, algorithmic techniques, and practical insights needed to understand the future of intelligent autonomous systems.Whether you are a student, researcher, engineer, educator, or AI professional, this series offers a comprehensive roadmap to mastering strategic artificial intelligence and multi-agent intelligence.

How do machines predict future outcomes? Why do some models generalize well while others fail? How can AI systems estimate uncertainty, explain their predictions, and operate reliably in real-world environments?Linear and Nonlinear Regression in Artificial Intelligence: Mathematical Foundations, Regularization Techniques & Predictive Modeling (Complete Bundle Edition) provides a comprehensive exploration of predictive intelligence—from classical linear regression to advanced Bayesian models, Gaussian Processes, neural network regression, kernel methods, explainable AI, and large-scale machine learning systems.Combining mathematical rigor, practical machine learning techniques, real-world case studies, and modern AI applications, this two-volume collection equips readers with the knowledge required to design, evaluate, interpret, and deploy predictive models across healthcare, finance, engineering, business analytics, scientific research, and next-generation AI systems.Whether you are a student, researcher, data scientist, or AI professional, this bundle offers a complete roadmap to mastering the science of prediction.

earning agents learn through exploration? How do generative AI systems create realistic content? And why is randomness one of the most powerful ingredients of intelligence itself?Stochastic Processes in Artificial Intelligence: Foundations, Algorithms, and Applications (Complete Bundle Edition) provides a comprehensive journey through the mathematics, algorithms, and real-world applications of probabilistic AI.From probability theory, Markov Chains, Hidden Markov Models, stochastic optimization, and reinforcement learning to Bayesian deep learning, probabilistic graphical models, Kalman filtering, robotics, generative AI, stochastic differential equations, and future research directions, this two-volume collection reveals how uncertainty drives intelligent behavior.Combining rigorous mathematics, intuitive explanations, practical algorithms, case studies, and cutting-edge AI applications, this bundle is an essential resource for students, researchers, engineers, and professionals seeking mastery of probabilistic intelligence and modern AI systems.

How do voice assistants understand speech? How do robots navigate uncertain environments? How are genes discovered in DNA sequences? And why do Hidden Markov Models remain relevant in the age of Transformers and Generative AI?Hidden Markov Models and AI: Sequential Data, Speech Recognition & NLP Applications (Complete Bundle Edition) takes readers on a comprehensive journey through the mathematics, algorithms, applications, and future of probabilistic sequence modeling.Covering everything from Markov Chains, Bayesian inference, Viterbi decoding, and Baum-Welch training to speech recognition, natural language processing, bioinformatics, robotics, cybersecurity, finance, and modern AI research, this three-volume collection bridges classical AI foundations with contemporary intelligent systems.Packed with mathematical rigor, practical examples, implementation projects, industry case studies, and future research directions, this bundle is an essential resource for students, researchers, AI engineers, speech scientists, NLP practitioners, and data professionals seeking mastery of sequential intelligence.

What if chaos is not a problem to eliminate—but a resource to harness?Modern Artificial Intelligence increasingly exhibits behaviors that resemble complex natural systems: adaptation, emergence, self-organization, unpredictability, and nonlinear learning dynamics.Nonlinear Dynamics and Chaos Theory in Artificial Intelligence (Complete Bundle Edition) explores the fascinating intersection of chaos theory, fractal geometry, complexity science, nonlinear mathematics, and intelligent adaptive systems.From bifurcations and strange attractors to chaotic neural networks, reinforcement learning, adaptive robotics, fractal architectures, and future AGI research, this two-volume collection reveals how nonlinear dynamics may become one of the most important foundations of next-generation AI.Combining rigorous mathematics, practical simulations, Python implementations, real-world case studies, and cutting-edge research directions, this bundle offers readers a unique journey into the science of complexity and intelligent behavior.If you want to understand not only how AI learns—but why intelligent systems evolve, adapt, and sometimes behave unpredictably—this bundle is your guide to the mathematics of adaptive intelligence.

Can machines truly reason?While modern AI excels at learning from data, genuine intelligence requires something more powerful—the ability to represent knowledge, draw logical conclusions, verify correctness, explain decisions, and prove solutions.Mathematical Logic and AI Reasoning: Foundations, Formal Methods & Automated Theorem Proving (Complete Bundle Edition) takes readers on a comprehensive journey through the mathematical foundations of machine reasoning.From propositional logic and automated theorem proving to SAT solvers, formal verification, intelligent agents, ontologies, Prolog, neuro-symbolic AI, and explainable reasoning systems, this two-volume collection reveals the logical engines that drive trustworthy Artificial Intelligence.Designed for students, researchers, engineers, educators, and AI professionals, this bundle combines rigorous theory, practical applications, modern tools, and cutting-edge research directions.If the future of AI depends on systems that can reason, verify, explain, and justify their decisions, this bundle provides the foundation for understanding—and building—that future.