Machine Learning Vol 1 (AI and ML Reference handbooks) - Softcover

Inhaltsangabe

Machine Learning Volume 1: Foundations, Supervised Learning, and Model Evaluation

Artificial Intelligence begins with Machine Learning. Before building deep learning models or deploying production AI systems, every practitioner needs a solid understanding of the principles that make machines learn from data.

Machine Learning Volume 1: Foundations, Supervised Learning, and Model Evaluation provides a structured, practical reference for developers, data scientists, AI engineers, students, and technical professionals seeking a clear understanding of modern machine learning.

Rather than overwhelming readers with unnecessary theory, this volume focuses on the concepts, algorithms, mathematics, workflows, and evaluation techniques that form the foundation of every successful ML system.

Inside this volume, you'll explore:

• Machine Learning fundamentals and terminology
• Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
• End-to-end ML workflow
• Data preprocessing and feature engineering
• Data cleaning and handling missing values
• Feature scaling, normalization, and encoding
• Training, validation, and testing strategies
• Regression algorithms and practical applications
• Classification algorithms and decision boundaries
• k-Nearest Neighbors (KNN)
• Naive Bayes
• Decision Trees
• Random Forest fundamentals
• Linear and Logistic Regression
• Bias–Variance tradeoff
• Overfitting and underfitting
• Cross-validation techniques
• Hyperparameter tuning fundamentals
• Performance metrics for regression
• Performance metrics for classification
• Precision, Recall, F1-Score, ROC, and AUC
• Confusion Matrix interpretation
• Model selection strategies
• Explainability basics
• Reproducible ML workflows
• Common beginner mistakes and practical best practices

Designed as both a learning resource and a long-term technical reference, this book presents complex topics through clear explanations, practical examples, comparison tables, diagrams, and concise summaries that make difficult concepts easier to understand.

Whether you're preparing for interviews, building your first machine learning project, transitioning into AI engineering, or strengthening your technical foundation before moving into deep learning, this volume provides the knowledge needed to progress with confidence.

Machine Learning Volume 1 is the first book in the AI/ML Reference Series, a comprehensive collection covering modern machine learning, deep learning, production AI, MLOps, Agentic AI, Generative AI, and real-world intelligent systems.

Build the right foundation. Master the core principles. Create machine learning systems with confidence.

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