Duration of the online course: 25 hours and 26 minutes
Artificial Intelligence is no longer just a buzzword; it is a set of practical ideas that help computers reason, search for solutions, and make decisions when information is incomplete. This free online course is designed to give you a solid foundation in AI concepts that still power modern applications, from planning and optimization to reasoning systems and probabilistic models. If you are starting in technology or programming and want a clear pathway into Artificial Intelligence and Machine Learning, this course helps you understand how AI works under the hood, not just how to use ready-made tools.
You will learn to frame real problems in a way an AI system can solve, modeling them as state spaces and exploring different strategies to find solutions efficiently. You will see why some approaches rely on little knowledge beyond the problem definition, while others gain speed by using heuristics and informed guidance. Along the way, you will strengthen your intuition for how AI chooses actions, balances trade-offs such as time and memory, and avoids unnecessary work when navigating large search spaces.
The course also builds the reasoning side of AI, showing how knowledge can be represented with formal languages and how logical inference can produce reliable conclusions. You will move from basic logical statements to more expressive structures that can describe objects, relationships, and rules. You will also discover how automated reasoning works in practice, including how systems derive answers, manage inference steps, and apply procedural control to make reasoning tractable.
Because real-world data is often noisy or incomplete, you will study reasoning under uncertainty and learn how probabilistic thinking helps AI make better decisions. By understanding Bayes rule and the intuition behind Bayesian networks, you will be able to interpret uncertainty, combine evidence, and model dependencies in a way that is both structured and explainable. The included exercises help reinforce each topic, so you can confidently connect theory to problem-solving and build a strong base for future learning in machine learning, data science, or intelligent systems development.
Video class: Fundamentals of Artificial Intelligence [Introduction]
04m
Video class: Lec 01: Introduction to AI
35m
Exercise: What are the four dimensions in which artificial intelligence systems can be understood according to the lecture content?
Video class: Lec 02: Problem Solving as State Space Search
57m
Exercise: Which of the following statements best describes an AI technique?
Video class: Lec 03: Uniformed Search
47m
Exercise: What is the primary characteristic of uninformed search strategies in the context of artificial intelligence problem-solving as state space search?
Video class: Lec 04: Heuristic Search
35m
Exercise: What is an admissible heuristic in the context of heuristic functions in AI search?
Video class: Lec 05: Informed Search
46m
Exercise: What is the main advantage of informed search strategies over uninformed search strategies in artificial intelligence?
Video class: Lec 06: Constraint Satisfaction Problems
1h06m
Exercise: Which of the following best describes a Constraint Satisfaction Problem (CSP) in the context of Artificial Intelligence?
Video class: Lec 07: Searching AND/OR Graphs
40m
Exercise: What is the primary advantage of using AO* algorithm for searching AND-OR graphs compared to best-first search?
Video class: Lec 08: Game Playing
44m
Exercise: Which of the following is a characteristic of board games used in artificial intelligence research?
Video class: Lec 09: Minimax Alpha-Beta
42m
Exercise: What is the primary purpose of the alpha-beta pruning algorithm when applied to the minimax algorithm in game tree evaluation?
Video class: Lec 10: Introduction to Knowledge Representation
36m
Exercise: What is the foundational choice of a formal language for knowledge representation and reasoning in AI, and why is it chosen?
Video class: Lec 11: Propositional Logic
34m
Exercise: Which statement is an example of a proposition in propositional logic?
Video class: Lec 12: First Order Logic -I
45m
Exercise: Which of the following statements best describes the difference between propositional logic and first order logic?
Video class: Lec 13: First Order Logic -II
40m
Exercise: What makes first-order logic more expressive than propositional logic?
Video class: Lec 14: Inference in First Order Logic - I
53m
Exercise: Which of the following distinguishes first-order logic from higher-order logics?
Video class: Lec 15: Inference in FOL - II
59m
Exercise: In knowledge representation and reasoning, which process involves converting first order predicate calculus statements to a certain form before applying resolution?
Video class: Lec 16: Answer Extraction
33m
Exercise: In the context of knowledge representation and reasoning, what is the purpose of answer extraction in first order logic theorem proving systems?
Video class: Lec 17: Procedural Control of Reasoning
44m
Exercise: Which strategy in resolution-based reasoning involves evaluating the truth value of literals by attaching procedures for computation, rather than including these literals or their negations directly in the base set?
Video class: Lec 18: Reasoning under Uncertainty
43m
Exercise: What does the Bayes' rule allow an AI system to compute in terms of probabilistic inference?
Video class: Lec 19: Bayesian Network
47m
Exercise: What task does a Bayesian network primarily assist in when dealing with uncertainty in AI systems?
25 hours and 26 minutes of online video course
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
Ready to get started?Download the app and get started today.
Install the app now
to access the courseOver 5,000 free courses
Programming, English, Digital Marketing and much more! Learn whatever you want, for free.
Study plan with AI
Our app's Artificial Intelligence can create a study schedule for the course you choose.
From zero to professional success
Improve your resume with our free Certificate and then use our Artificial Intelligence to find your dream job.
You can also use the QR Code or the links below.

Free CourseDeep Learning With PyTorch
3h39m
19 exercises

Free CourseChat GPT and OpenAI API course
5h17m

Free CourseMachine Learning tutorial
10h20m
6 exercises

Free CourseData Science
5h58m
38 exercises

Free CourseArtificial intelligence
12h40m
7 exercises

Free CourseData Science full course
11h22m

Free CourseMachine Learning for complete beginners
1h09m
17 exercises

Free CourseGoogle Prompting Essentials
3h24m
10 exercises

Free CourseGenerative AI
1h43m
11 exercises

Free CourseR programming for Data Science
1h07m
6 exercises
Thousands of online courses in video, ebooks and audiobooks.
To test your knowledge during online courses
Generated directly from your cell phone's photo gallery and sent to your email
Download our app via QR Code or the links below::.
+ 10 million
students
Free and Valid
Certificate
60 thousand free
exercises
4.8/5 rating in
app stores
Free courses in
video and ebooks
Course comments: Fundamentals of Artificial Intelligence
Prasanth Kalavakuri
this is very useful course
Aakash Kumar
IITian teacher are great and telling about next level of idea.
Aditi Dwivedi
nice