Machine learning is most successful when it augments rather than replaces the specialized knowledge of a subject-matter expert. It works with medical doctors at the forefront of the fight to eradicate cancer, assists engineers and programmers with our efforts to create smarter homes and automobiles, and helps social scientists build knowledge of how societies function. Toward these ends, it is employed in countless businesses, scientific laboratories, hospitals, and governmental organizations. Any organization that generates or aggregates data likely employs at least one machine learning algorithm to help make sense of it.
Though it is impossible to list every use case of machine learning, a survey of recent
success stories includes several prominent applications:
• Identification of unwanted spam messages in e-mail
• Segmentation of customer behavior for targeted advertising
• Forecasts of weather behavior and long-term climate changes
• Reduction of fraudulent credit card transactions
• Actuarial estimates of financial damage of storms and natural disasters
• Prediction of popular election outcomes
• Development of algorithms for auto-piloting drones and self-driving cars
• Optimization of energy use in homes and office buildings
• Projection of areas where criminal activity is most likely
• Discovery of genetic sequences linked to diseases
By the end of this book, you will understand the basic machine learning algorithms that are employed to teach computers to perform these tasks. For now, it suffices to say that no matter what the context is, the machine learning process is the same.Regardless of the task, an algorithm takes data and identifies patterns that form the basis for further action.