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References: Machine Learning Foundations

  1. Machine Learning — Wikipedia https://en.wikipedia.org/wiki/Machine_learning A broad overview of machine learning, covering supervised and unsupervised learning, model training, and real-world applications. An essential starting point for understanding how computers learn from data.

  2. Supervised Learning — Wikipedia https://en.wikipedia.org/wiki/Supervised_learning Explains how supervised learning uses labeled training data to teach models to classify inputs or predict values. Directly relevant to this chapter's coverage of classification and regression tasks.

  3. Feature (Machine Learning) — Wikipedia https://en.wikipedia.org/wiki/Feature_(machine_learning) Describes what features are in a dataset and why choosing the right features matters for model accuracy. Supports the chapter's introduction to how machines represent and interpret information about the world.

  4. Python Crash Course by Eric Matthes (No Starch Press, 3rd ed.) Chapters on data and lists build the foundation needed to organize training data and feature sets in Python, making this book a practical companion for students beginning to work with structured datasets.

  5. Python for Kids by Jason R. Briggs (No Starch Press) Written for the same age group as this textbook, it introduces Python data structures and functions in a playful style that eases the transition from Scratch to the kind of list-based thinking machine learning requires.

  6. scikit-learn: Getting Started — Real Python https://realpython.com/machine-learning-with-python/ A beginner-friendly walkthrough of building and evaluating machine learning models in Python. Covers training data, labels, and model evaluation concepts introduced in this chapter with working code examples.

  7. Python Lists Tutorial — W3Schools https://www.w3schools.com/python/python_lists.asp Lists are the primary structure for storing features and labels in beginner ML programs. This page provides clear, interactive examples for creating, indexing, and looping over the lists that hold training data.

  8. Python Machine Learning Introduction — GeeksforGeeks https://www.geeksforgeeks.org/machine-learning/ Covers core concepts including supervised vs. unsupervised learning, classification, and regression with diagrams and code. A reliable reference for students who want a second explanation of any term from this chapter.

  9. Python for Loop — Programiz https://www.programiz.com/python-programming/for-loop Iterating over training datasets is one of the first skills needed in machine learning. This tutorial explains for-loops with clear examples that directly apply to processing lists of features and labels.

  10. Introduction to Python — Khan Academy Computing https://www.khanacademy.org/computing/intro-to-python-fundamentals An interactive, self-paced Python course covering variables, data types, and functions — the building blocks students need before writing their first machine learning programs in Python.