• Length:
    7 Weeks
  • Effort:
    10–30 hours per week
  • Price:

    FREE
    Add a Verified Certificate for $199 USD

  • Institution
  • Subject:
  • Level:
    Introductory
  • Language:
    English
  • Video Transcript:
    English
  • Course Type:
    Self-paced on your time

Associated Programs:

Prerequisites

CS50 or prior programming experience in Python.

About this course

Skip About this course

AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

What you'll learn

Skip What you'll learn
  • graph search algorithms
  • adversarial search
  • knowledge representation
  • logical inference
  • probability theory
  • Bayesian networks
  • Markov models
  • constraint satisfaction
  • machine learning
  • reinforcement learning
  • neural networks
  • natural language processing

Meet your instructors

David J. Malan
Gordon McKay Professor of the Practice of Computer Science
Harvard University
Brian Yu
Senior Preceptor in Computer Science
Harvard University

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