30+ AI Models, Explained and Coded

Stop reading about AI. Start building it.

Most AI books either bury you in mathematics or hand you code you can’t explain. This book does both jobs properly: it teaches you how each model thinks, then shows you the Python code to make it work.

What’s inside

• 40 AI models explained: Linear and Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, K-Means, PCA, ARIMA, CNN, ResNet, MobileNet, EfficientNet, U-Net, LSTM, GRU, GANs, Vision Transformers, BERT, GPT, Llama, Mistral, Qwen, CLIP, Whisper, AI agents, Q-Learning, DQN, PPO and more.
• 61 Python programs: complete code that you can copy, run and modify. Most run offline on a normal laptop, using built-in or generated data.
• 56 diagrams and illustrations: architecture drawings, real-world scenarios and charts that make hard ideas easy to see.
• A consistent chapter structure: what the model is, how it works, a practical example, the code, a line-by-line walkthrough, advantages, limitations, a project idea and practice exercises.
• 10 final-year project blueprints: each with the problem, models, data, a step-by-step work plan, evaluation measures and risks, ready to adapt for your college submission.
• Build and deploy a real application: turn a model into a working app with FastAPI, Streamlit and Docker.
• Choosing the right model: a practical decision guide, plus an honest chapter on evaluation so your results are trustworthy.
• Bonus material: a troubleshooting table, a glossary, dataset suggestions and a project documentation checklist.

Real-world examples you’ll recognise: plant disease detection, road crack detection, fraud detection, traffic monitoring, predictive maintenance, electricity demand forecasting, a college document chatbot, lecture transcription and more.

Perfect for

• BTech, BE, MCA and postgraduate students preparing final-year projects
• Computer Science, IT, Electronics and Mechanical engineering students
• Python developers who want to move into AI
• Beginners who want a clear route from basics to modern generative AI

What you’ll be able to do after reading

• Choose the right model for a real problem and explain why
• Train, evaluate and compare models without common mistakes such as data leakage
• Fine-tune pretrained models for images and text
• Build a retrieval-based chatbot over your own documents
• Wrap a model in a working web application and present it confidently in your viva

Requires basic Python knowledge. Code examples use Python with scikit-learn, TensorFlow/Keras, PyTorch and Hugging Face. Some chapters use optional paid APIs or downloads of pretrained models.

Start with one chapter today and have your first working model running tonight.