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Full Description
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 DescriptionMachine 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 forThis 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.
Contents
Table of Contents
Introduction to Data Science and the Python Ecosystem
Statistics, Probability, and Linear Models
Core Machine Learning Algorithms
Feature Engineering and Data Preprocessing
Introduction to Neural Networks
Building and Training Deep Networks
Computer Vision with Convolutional Networks
Transformers and Modern NLP
Recommender Systems
Evaluating and Interpreting Models
Optimization and Experiment Tracking
Deploying Models into Production
Scaling, Automation, and MLOps Pipelines
Generative Models and Autoencoders
Large Language Models and RAG Systems
Building LLM Agents and Multi-Agent Systems
Ethics, Fairness, and Responsible AI
