Machine Learning Engineering

BUILD MACHINE LEARNING SYSTEMS - NOT JUST MODELS

Machine Learning is no longer just about training an algorithm and measuring its accuracy.

Modern Machine Learning engineers need to understand the complete journey from business problem and raw data to model development, evaluation, deployment, monitoring, and continuous improvement.

Machine Learning Engineering provides a practical path through that journey, combining Machine Learning fundamentals with the engineering practices required to build reliable, production-ready ML systems.

Inside this book, you will learn how to work with:

• Machine Learning fundamentals
• Python and data preparation
• Statistics and probability
• Exploratory Data Analysis
• Regression algorithms
• Classification algorithms
• Decision Trees and ensemble learning
• Feature engineering
• Model selection
• Model evaluation
• Hyperparameter tuning
• Machine Learning pipelines
• MLOps fundamentals
• Model monitoring
• Data drift and concept drift
• Model deployment
• Production optimization
• Model interpretability
• Advanced Machine Learning techniques
• End-to-End Machine Learning projects

Throughout the book, NovaShop, a fictional e-commerce company, is used to connect concepts with realistic business problems such as revenue prediction and customer churn.

Rather than focusing only on algorithm definitions, the book emphasizes practical decision-making:

What data should be used?
Which features are reliable?
Which metric actually matters?
Is the model generalizing?
How should it be deployed?
How should it be monitored?
When should it be retrained?

The final project brings these ideas together into an end-to-end Machine Learning system covering data preparation, feature engineering, model training, evaluation, tuning, pipelines, deployment, monitoring, and retraining.

Whether you are a student, developer, aspiring Data Scientist, Machine Learning Engineer, or technology professional, this book is designed to help you move from understanding Machine Learning concepts to thinking like an ML engineer.

Learn the fundamentals.
Build the models.
Engineer the system.