End-to-End Machine Learning: A Practical Guide from Foundations to Production MLOps

Build machine learning systems-not just machine learning models.End-to-End Machine Learning is a practical guide for readers who want to progress from machine learning fundamentals to the engineering practices required to put models into production.Written for beginner and intermediate practitioners, the book develops machine learning concepts step by step while connecting algorithms to the broader lifecycle of data preparation, model evaluation, deployment, monitoring, and MLOps.Readers will learn how to: - Frame machine learning problems and establish meaningful baselines - Explore, clean, preprocess, and engineer features from real-world data - Understand the mathematical intuition behind important ML algorithms - Build Linear, Ridge, Lasso, Logistic Regression, kNN, Decision Tree, and Random Forest models - Apply XGBoost and LightGBM to structured machine learning problems - Evaluate models using appropriate regression and classification metrics - Use cross-validation and hyperparameter tuning correctly - Handle imbalanced classification and probability thresholds - Apply K-Means clustering, hierarchical clustering, and PCA - Understand time-series forecasting, lag features, and chronological validation - Build reproducible training and inference pipelines - Track experiments and manage models with MLflow and model registries - Deploy models using APIs, containers, Docker, and cloud architectures - Implement CI/CD, monitoring, drift detection, retraining, and production MLOpsGuided projects using publicly accessible datasets help connect the concepts to practical implementation, while architecture diagrams, model-comparison guides, exercises, solutions, and production checklists reinforce the material.The result is a practical learning path from your first machine learning workflow to a production-ready ML system.Ideal for: aspiring data scientists, ML engineers, data engineers, software engineers, technical professionals, and technology leaders who want a structured understanding of modern machine learning from foundations through production.