// FAQs
No, machine learning is one specific approach used to achieve artificial intelligence, focused on systems that learn from data, while artificial intelligence is the broader concept of machines performing tasks that typically require human intelligence.
No, understanding the core concepts of machine learning does not require coding knowledge, though building or working with machine learning systems directly typically does require some programming skills.
Yes, machine learning systems can make mistakes, particularly when faced with data that differs significantly from what they were trained on, or when the underlying training data itself contains errors or biases.
Supervised learning uses data that includes correct answers to learn from, while unsupervised learning identifies patterns and structures within data that does not include predefined correct answers.
Machine learning powers many familiar features, including email spam filtering, personalized content recommendations, voice assistant understanding and fraud detection systems used by banks.