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1 results

  • 7 - Machine Learning Introduction and Regression

  • from Part III - Machine Learning for Data Science
  • Chirag Shah, University of Washington
  • Book:
    A Hands-On Introduction to Data Science with Python
    Published online:
    07 February 2026
    Print publication:
    22 January 2026, pp 183-209

  • Summary

    This chapter introduces machine learning as a subset of artificial intelligence that enables computers to learn from data without explicit programming. It defines machine learning using Tom Mitchell’s formal framework and explores practical applications like self-driving cars, optical character recognition, and recommendation systems. The chapter focuses on regression as a fundamental machine learning technique, explaining linear regression for modeling relationships between variables. A key section covers gradient descent, an optimization algorithm that iteratively finds the best model parameters by minimizing error functions. Through hands-on Python examples, students learn to implement both linear regression and gradient descent algorithms, visualizing how models improve over iterations. The chapter emphasizes practical considerations for choosing appropriate algorithms, including accuracy, training time, linearity assumptions, and the number of parameters, preparing students for more advanced supervised and unsupervised learning techniques.