Learn the most in-demand AI skills through just 4 comprehensive eBook!
Want to go beyond classroom theory and build real-world AI skills.
Looking to integrate AI, ML, and GenAI into their tech toolkit.
Need a clear, structured path to become interview-ready for AI roles.
Want to leverage AI tools for content creation, coding, automation, and scaling their business.
Foundations: Introduction, Intelligent Agents
Search & Problem Solving: Classical Search, Beyond Search, Adversarial Search, Constraint Satisfaction
Knowledge & Logic: Logical Agents, First-Order Logic, Inference, Knowledge Representation
Planning & Decision Making: Classical Planning, Acting in the Real World, Uncertainty, Probabilistic Reasoning, Simple & Complex Decisions
Learning: From Examples, Knowledge in Learning, Probabilistic Models, Deep Learning, Reinforcement Learning
Natural Language & Perception: NLP for Communication, Perception
Robotics: Planning, Sensing, and Acting in Real Environments
Philosophy & Future: Philosophical Foundations, The Present & Future of AI
ML Basics: Landscape, End-to-End Projects, Classification
Modeling: Training Models, SVMs, Decision Trees, Ensembles & Random Forests
Advanced Techniques: Dimensionality Reduction, Unsupervised Learning
Neural Networks: ANN with Keras, Deep Nets Training, TensorFlow Custom Models
Deep Learning: Data Preprocessing, CNNs for Vision, RNNs for Sequences
NLP: RNNs, Attention, Transformers, Pretraining
Generative AI: Autoencoders, GANs, Representation Learning
Other Key Areas: Reinforcement Learning, Latest ML Trends
Foundations: What Is Generative Deep Learning?, TensorFlow & Keras Basics
GANs (Generative Adversarial Networks): Intro to GANs, DCGANs, Improved Training, Conditional GANs, Pix2Pix, CycleGAN, BigGAN, Progressive Growing, StyleGAN & StyleGAN2
VAEs & Autoencoders: Variational Autoencoders, Collaborative Filtering Applications
Transformers & Large Models: Transformers, GPT & GPT-2, BERT, Multimodal AI, MuseNet & Music Generation
Diffusion Models: DDPMs (Denoising Diffusion Probabilistic Models)
Applications: Generative AI in Science & Real-World Use Cases
Future Outlook: Trends & The Road Ahead
Foundations: Introduction, Linear Algebra, Probability & Information Theory, Numerical Computation, ML Basics
Core Models: Feedforward Networks, Regularization, Optimization for Training
Architectures: Convolutional Networks (CNNs), Sequence Models (RNNs & Recursive Nets)
Methodology & Applications: Practical Strategies, Real-World Use Cases
Representation Learning: Autoencoders, Linear Factor Models, Structured Probabilistic Models
Generative Models: Basic & Advanced Deep Generative Models, Monte Carlo Methods, Approximate Inference, Partition Function Challenges
Wrap-Up: Conclusions & Outlook




Chapter Insights




Chapter Insights




Chapter Insights




Chapter Insights
Worth ₹2000/-
Worth ₹2000/-
Worth ₹2000/-
You’ll get 4 premium AI/ML eBooks covering Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI, plus bonus resources for projects and interview prep.
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Yes. The bundle includes hands-on learning with Python, Scikit-learn, TensorFlow, Keras, and GenAI tools, not just theory.
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