Introduction to Artificial Intelligence with Python

Understand what artificial intelligence is, how intelligent agents solve problems, and how Python can be used to build AI systems.

Learn how AI systems search through possible solutions using depth-first search, breadth-first search, greedy best-first search, and A* search.

Understand how AI makes decisions in competitive environments using minimax, game trees, and alpha-beta pruning.

Learn how AI systems represent facts and information using propositional logic, knowledge bases, and logical statements.

Understand how AI can derive new information from existing knowledge using model checking, inference, logical equivalence, and resolution.

Learn how AI systems handle incomplete or uncertain information using probability, conditional probability, and Bayes' rule.

Understand how Bayesian networks represent relationships between variables and use probability to reason about uncertain situations.

Use joint probability, conditional probability, sampling, and probabilistic reasoning to make predictions from uncertain data.

Learn how AI systems find good solutions efficiently using local search, hill climbing, simulated annealing, and optimization techniques.

Understand constraint satisfaction problems and use backtracking search to solve problems with multiple possible combinations.

Understand how computers learn from data and explore the differences between supervised, unsupervised, and reinforcement learning.

Learn how AI systems classify data using algorithms such as nearest-neighbor classification, perceptrons, and support vector machines.

Understand regression, loss functions, and how machine learning models can make numerical predictions from data.

Learn why machine learning models can perform poorly on new data and how regularization and other techniques help improve generalization.

Understand how AI agents learn by interacting with an environment, receiving rewards, and improving their decisions through experience.

Learn how artificial neural networks work, including neurons, layers, activation functions, weights, and biases.

Understand how neural networks learn by adjusting their parameters using gradient descent and backpropagation.

Explore deeper neural network architectures and understand how multiple layers can learn increasingly complex patterns from data.

Learn how convolutional neural networks process images and identify visual patterns such as shapes, edges, and objects.

Understand how AI systems process human language and explore techniques for analyzing and interpreting text.

Learn how language models represent and predict sequences of words using techniques such as n-grams and probabilistic models.

Build systems that classify and analyze text using techniques such as Naive Bayes and TF-IDF.

Apply AI concepts through practical Python projects involving search, games, probability, machine learning, neural networks, and natural language processing.

Combine the concepts learned throughout the course to build and present a practical AI project while explaining the decisions and techniques used.