HarvardX: CS50's Introduction to Artificial Intelligence with Python

Learn to use machine learning in Python in this introductory course on artificial intelligence.

7 weeks 10–30 hours per week - Self-paced Progress at your own speed - Optional upgrade available

1,058,536 already enrolled!

About this course

This course 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, machine learning, large language models, and other topics in artificial intelligence 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.

At a glance

• Institution: HarvardX
• Subject: Computer Science
• Level: Introductory
• Prerequisites:
• CS50 or prior programming experience in Python.
• Associated programs:
• Professional Certificate in Computer Science for Artificial Intelligence

• Language: English
• Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文

• Associated skills:Mathematical Optimization, Search Algorithms, Algorithms, Artificial Intelligence, Computer Science, Machine Translation, Reinforcement Learning, Python (Programming Language), Intelligent Systems, Handwriting Recognition, Machine Learning, Proofreading, Medical Diagnosis

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

+ YOUR GIFT COURSE

HarvardX: CS50's Introduction to Game Development

Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design of games such as Super Mario Bros., Pokémon, Angry Birds, and more.

727,448 already enrolled!

About this course

Skip About this course

This course 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, machine learning, large language models, and other topics in artificial intelligence 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.

About more this course

In a quest to understand how video games themselves are implemented, you'll explore the design of such childhood games as:

• Super Mario Bros.
• Pong
• Flappy Bird
• Breakout
• Match 3
• Legend of Zelda
• Angry Birds
• Pokémon
• 3D Helicopter Game
• Dreadhalls
• Portal

At a glance

• Institution: HarvardX
• Subject: Computer Science
• Level: Introductory
• Prerequisites:
• CS50 or prior programming experience in Python.

• Associated programs:
• Professional Certificate in Computer Science for Artificial Intelligence

• Language: English
• Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文

• Associated skills:Mathematical Optimization, Search Algorithms, Algorithms, Artificial Intelligence, Computer Science, Machine Translation, Reinforcement Learning, Python (Programming Language), Intelligent Systems, Handwriting Recognition, Machine Learning, Proofreading, Medical Diagnosis

What you'll learnkip 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