Machine Learning Vol 1

By Rashmi Patel

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 terminologyTypes of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learningEnd-to-end ML workflowData preprocessing and feature engineeringData cleaning and handling missing valuesFeature scaling, normalization, and encodingTraining, validation, and testing strategiesRegression algorithms and practical applicationsClassification algorithms and decision boundariesk-Nearest Neighbors (KNN)Naive BayesDecision TreesRandom Forest fundamentalsLinear and Logistic RegressionBias-Variance tradeoffOverfitting and underfittingCross-validation techniquesHyperparameter tuning fundamentalsPerformance metrics for regressionPerformance metrics for classificationPrecision, Recall, F1-Score, ROC, and AUCConfusion Matrix interpretationModel selection strategiesExplainability basicsReproducible ML workflowsCommon 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.

• Format: Paperback
• Language: English
• ISBN: B0HC65LSZ9
• ISBN13: 9798187809837
• Release Date: July 2026
• Publisher: Independently Published
• Length: 210 Pages
• Weight: 0.63 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"