Introduction to Machine Learning

Definition

• Machine learning (ML) is a field of study that gives computers the ability to learn without being explicitly programmed.
• Attributed to Arthur Samuel in the 1950s.

Example: Checkers Program

• Arthur Samuel created a checkers-playing program.
• Samuel was not a skilled checkers player himself.
• Program played tens of thousands of games against itself.
• Learned from game outcomes to improve performance.
• Eventually became better at checkers than Samuel.

Interactive Learning

• Lectures will occasionally include questions (quizzes).
• Quizzes help reinforce learning even if answers are incorrect initially.
• Emphasis on practice and understanding rather than just correctness.

Types of Machine Learning

• Two main types:
  • Supervised Learning
  • Unsupervised Learning

Supervised Learning

• Most used in real-world applications.
• Rapid advancements and innovations.
• Focus of the first and second courses in the specialization.

Unsupervised Learning

• Focus of the third course in the specialization.

Recommender Systems and Reinforcement Learning

• Also covered in the third course.
• Important but less commonly used than supervised learning.

Practical Advice and Best Practices

• Understanding not just tools but also their application is vital.
• Practical advice on applying ML algorithms effectively.
• Designing and building practical, valuable ML systems.

Real-world Applications

• Importance of avoiding common pitfalls.
• Example: Experienced ML teams sometimes struggle with ineffective strategies.
• Course aims to provide best practices to avoid such issues.

Course Goals

• Equip students with tools and application skills.
• Develop a sense of the best practices in ML.
• Aim to produce skilled ML engineers who can design and build serious ML systems.

Next Steps

• Upcoming videos will delve deeper into supervised and unsupervised learning.
• Discussion on when to use each type of learning.