Artificial Intelligence Essentials
Master AI fundamentals through philosophy, science fiction, and hands-on Python programming with search algorithms.
Master AI fundamentals through philosophy, science fiction, and hands-on Python programming with search algorithms.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI and Machine Learning Essentials with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.3
(11 ratings)
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What you'll learn
Understand AI's philosophical and historical context
Master common search algorithms including A* Search
Implement rational agents and task environments
Develop Python programming skills for AI applications
Analyze and solve complex search problems
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
12 assignments
Access on Mobile, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores artificial intelligence fundamentals through both theoretical and practical lenses. Starting with philosophical foundations and science fiction perspectives, students progress to practical implementation of AI concepts. The curriculum covers rational agents, task environments, and various search algorithms including uninformed methods like breadth-first and depth-first search, as well as informed techniques like A* search. Through hands-on Python programming assignments, students gain practical experience implementing these algorithms while understanding their theoretical underpinnings.
Artificial Intelligence Overview
Module 1 · 3 Hours to complete
Task Environment and Python Review
Module 2 · 5 Hours to complete
Uninformed Search
Module 3 · 5 Hours to complete
Informed Search
Module 4 · 4 Hours to complete
Fee Structure
Instructor
Leading Scholar in Natural Language Processing and Crowdsourcing
Chris Callison-Burch is an associate professor of Computer and Information Science at the University of Pennsylvania, where his work has positioned him as a thought leader in natural language processing (NLP) and crowdsourcing. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University, contributing significantly to advancements in the field during his six-year tenure.Chris has held prominent roles in major conferences and organizations, including serving as the General Chair of the ACL 2017 conference, Program Co-Chair for EMNLP 2015, Chair of the Executive Board of NAACL (2011–2013), and Secretary-Treasurer for SIGDAT (2015–2017). His editorial contributions span leading journals such as Transactions of the ACL (TACL) and Computational Linguistics.With over 100 publications that have been cited more than 10,000 times, Chris's research has had a profound impact on computational linguistics. Recognized as a Sloan Research Fellow, he has received prestigious faculty research awards from Google, Microsoft, Amazon, and Facebook. His work has also been supported by DARPA and the National Science Foundation (NSF).Chris's research interests focus on the intersections of natural language processing, machine translation, and the innovative use of crowdsourcing to tackle complex computational challenges. His contributions continue to shape the future of AI-driven language technologies and their practical applications.
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4.3 course rating
11 ratings
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