Machine Learning Fundamentals
Core Concepts, Models, and Practical Foundations
What's Included:
Key Highlights
- Concept-first approach to machine learning
- Clear explanations without heavy math
- Real-world ML project perspective
- Ethics and responsible AI coverage
- Strong foundation for further ML study
Overview
Learn machine learning from the ground up. Understand core concepts, data preparation, models, evaluation techniques, and real-world ML workflows-no advanced math required.
The Problem
Machine learning often feels overwhelming due to complex mathematics, unclear terminology, and fragmented learning resources.
The Solution
This book provides a clear, structured introduction to machine learning, focusing on understanding concepts, models, and workflows without unnecessary complexity.
About This Book
Understand Machine Learning from First Principles
Machine Learning Fundamentals is a beginner-friendly yet comprehensive introduction to the concepts, models, and workflows that power modern machine learning systems.
This book focuses on understanding how machine learning works, why specific models are used, and how real-world machine learning projects are structured.
What This Book Covers
- What machine learning really is—and what it is not
- Types of machine learning and their use cases
- Working with data and preparing it for models
- Core machine learning models and algorithms
- Training and evaluating models
- Feature engineering and pipelines
- Ethics, bias, and responsible machine learning
The book emphasizes clarity, intuition, and practical understanding over heavy mathematics, making it ideal for beginners.
Lucas Winfield
Who Is This Book For?
- Beginners interested in machine learning
- Students exploring AI and data science
- Developers transitioning into ML
- Analysts working with data
- Professionals seeking ML literacy
Who Is This Book NOT For?
- Advanced ML researchers
- Readers seeking deep mathematical proofs
- Those looking only for framework-specific tutorials
Table of Contents
- What Machine Learning Really Is
- Types of Machine Learning
- Data
- Data Preparation and Cleaning
- Linear Models
- Tree-Based Models
- Training Machine Learning Models
- Evaluating Model Performance
- Clustering Fundamentals
- Dimensionality Reduction
- Feature Engineering Basics
- Machine Learning Pipelines
- Tools and Libraries Overview
- Ethics, Bias, and Responsible ML
- Machine Learning in Real Projects
- Learning Path Beyond ML Fundamentals
Requirements
- Basic programming concepts
- High-school level math understanding
- Curiosity about data and intelligent systems
