Build complete machine learning and AI solutions with Python, from modeling and LLMs to deployment and MLOps. Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader
Key Features
- Build AI systems from data prep to LLM deployment
- Learn RAG pipelines, Context engineering, Agentic AI, and real MLOps tools
- Apply each concept using practical Python projects
Book Description
Machine Learning, Data Science, and AI Engineering with Python teaches you how to build and ship production-ready AI systems. Starting from core concepts in machine learning, data science, and Python tooling, you'll move through deep learning, Transformers, and large language models to master advanced tools like retrieval-augmented generation (RAG), LLM agents, and responsible AI workflows. With each chapter building toward a complete machine learning pipeline, you'll gain hands-on experience with tools like PyTorch, MLflow, DVC, and FastAPI. You'll also explore key production skills such as model versioning, A/B testing, and containerized deployment. By the end of this book, you'll know how to take a raw dataset and develop, evaluate, and deploy real time AI systems that are robust, scalable, and explainable.
What you will learn
- Train ML models using scikit-learn and PyTorch
- Build deep learning systems for vision and NLP tasks
- Integrate and fine-tune Transformer-based LLMs
- Construct RAG pipelines using vector databases
- Develop and deploy APIs with FastAPI and Docker
- Manage models and experiments with MLflow and DVC
- Build LLM agents using OpenAI, Gemini, LangGraph and ADK
- Apply fairness and interpretability to ML pipelines
Who this book is for
This book is for aspiring machine learning engineers, data scientists, and developers looking to gain real-world AI skills. Readers will go from Python basics to full-stack AI development, including model deployment, MLOps, and cutting-edge LLM integrations.
