Machine Learning, Data Science, and AI Engineering with Python
Build real world ML pipelines, deploy LLM in production, and scale AI applications with Python
Will be published approx. on 5. March 2027
Book
Paperback/Softback
978-1-80638-517-1 (ISBN)
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.
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.
More details
Persons
Frank Kane spent nine years at Amazon and IMDb, where he developed and managed the technology behind product and movie recommendations for millions of customers. Holding 17 patents in distributed computing, data mining, and machine learning, Frank's expertise is widely recognized. In 2012, he founded Sundog Software, a successful company that teaches big data analysis and has educated over a million students globally in machine learning, data engineering, and leadership. Dr. Gabriel Preda is a Principal Data Scientist for Endava, a major software services company. He has worked on projects in various industries, including financial services, banking, portfolio management, telecom, and healthcare, developing machine learning solutions for various business problems, including risk prediction, churn analysis, anomaly detection, task recommendations, and document information extraction. In addition, he is very active in competitive machine learning, currently holding the title of a three-time Kaggle Grandmaster and is well-known for his Kaggle Notebooks.
Content
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
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