Build Train Deploy

Most machine learning books teach algorithms in order and abandon you the moment your model leaves the notebook. This one works the other way. You build 3 complete systems, from raw messy data to deployed APIs, learning each concept exactly when you need it. You will build a churn prediction service with Scikit-Learn, a plant disease classifier with Keras, and a support ticket classifier with PyTorch using transformer models from HuggingFace. You finish with 3 working projects and the judgment to pick the right framework.
Why does this matter? Fewer than 25% of trained machine learning models ever reach production, and the reason is rarely technical skill. Most training never covers deployment, cost-aware choices, or evaluation tied to business outcomes. This book closes that gap. It is for Python developers and data scientists tired of tutorials that end at an accuracy score. No math background or GPU needed, every project runs on a CPU, and transfer learning means small datasets deliver serious results. Prose-first, it builds judgment and workflow, not syntax that expires with the next library release.
You will learn how to:
• Clean, validate, and understand data, treating the 60% to 80% of project time that data work consumes as the core craft.
• Compare logistic regression, random forests, and gradient boosting, and tune them with Bayesian search.
• Evaluate models with precision, recall, and cost matrices translated into business $.
• Build CNNs and fine-tune ImageNet pretrained models with augmentation and Grad-CAM debugging.
• Fine-tune BERT models for text classification and deploy them with quantized serving.
• Ship models as FastAPI services in Docker with shadow, canary, and A/B rollouts.
• Monitor production with drift detection and manage versions in an MLflow registry.
The payoff is a portfolio you own: 3 deployed systems that prove real capability. If you are ready to cross the gap between knowing and doing, this guide takes you there.