A clear and beginner-focused guide to Python and ML fundamentals. Covers coding basics, OOP, and core machine learning methods in a friendly, structured format.
Key Features
- A two-part structure combining Python basics and machine learning for seamless skill-building
- Logical progression designed to reduce learning friction and build strong conceptual clarity
- Hands-on practice with Jupyter notebooks and real datasets to reinforce every key concept taught
Book Description
Starting with Python syntax and data types, this guide builds toward implementing key machine learning models. Learn about loops, functions, OOP, and data cleaning, then transition into algorithms like regression, KNN, and neural networks. A final section walks you through model optimization and building projects in Python. The book is split into two major sections—foundational Python programming and introductory machine learning. Readers are guided through essential concepts such as data types, variables, control flow, object-oriented programming, and using libraries like pandas for data manipulation. In the machine learning section, topics like model selection, supervised vs unsupervised learning, bias-variance, and common algorithms are demystified with practical coding examples. It's a structured, clear roadmap to mastering both programming and applied ML from zero knowledge.
What you will learn
- Master Python syntax, variables, and basic data structures
- Build control flows using conditionals, loops, and functions
- Implement object-oriented concepts like classes and objects
- Analyze and clean datasets using pandas and Python tools
- Train supervised and unsupervised machine learning models
- Evaluate and optimize models for better prediction accuracy
Who this book is for
This book is perfect for beginners with little to no coding or data science background. It assumes no prior experience with Python or machine learning. Ideal for aspiring data analysts, tech learners, and students transitioning into AI and programming roles.
