Machine learning, artificial intelligence, and data science are all related fields but have distinct differences.

Machine Learning:

Machine learning is a subset of artificial intelligence that focuses on enabling computers to learn and make decisions without being explicitly programmed. It involves developing algorithms that allow machines to learn from data and improve their performance over time.

For example, in spam email classification, machine learning algorithms can be trained on a large dataset of labeled emails to automatically classify incoming emails as either spam or not spam.

Artificial Intelligence:

Artificial intelligence (AI) refers to the broader concept of creating machines or systems that can perform tasks that would typically require human intelligence. It encompasses various techniques, including machine learning, natural language processing, computer vision, and more.

For instance, AI can be used in autonomous vehicles to enable them to perceive and understand their surroundings, make decisions, and navigate safely without human intervention.

Data Science:

Data science focuses on extracting insights and knowledge from large and complex datasets. It involves using various techniques, such as statistical analysis, machine learning, and data visualization, to uncover patterns, trends, and correlations in data.

For example, data scientists can analyze customer data to identify patterns and preferences, helping businesses make data-driven decisions and improve their products or services.

References:

• Tom M. Mitchell. "Machine Learning." McGraw Hill Education, 1997.
• Peter Norvig and Stuart Russell. "Artificial Intelligence: A Modern Approach." Pearson, 2016.
• Hadley Wickham and Garrett Grolemund. "R for Data Science." O'Reilly Media, 2016.