PX01 AI

PX01 AI – Introduction to Artificial Intelligence with Python

Course Overview:
PX01 AI is a comprehensive introduction to the foundations of Artificial Intelligence, designed for learners eager to understand how intelligent systems are built and applied using Python. The course blends theory with hands-on practice, guiding students through the essential principles, algorithms, and applications that define modern AI.

Throughout the course, participants will explore:

• Search and Problem-Solving: Learn how AI agents find solutions to puzzles, mazes, and real-world navigation tasks using algorithms like DFS, BFS, A*, and adversarial search for games.
• Knowledge Representation & Reasoning: Understand how machines can represent facts, reason logically, and make informed decisions using propositional and first-order logic.
• Uncertainty & Probabilistic Models: Master probability, Bayes’ rule, Bayesian networks, and Markov models to handle incomplete or uncertain information.
• Optimization: Dive into local search methods, simulated annealing, linear programming, and constraint satisfaction problems to discover how AI finds the best solutions.
• Machine Learning: Explore supervised, unsupervised, and reinforcement learning, covering techniques such as regression, classification, clustering, and policy optimization.
• Neural Networks: Study perceptrons, deep learning architectures, convolutional networks (CNNs), recurrent networks (RNNs), and Transformers—the backbone of modern AI breakthroughs.
• Natural Language Processing (NLP): Discover how AI processes human language through statistical models, embeddings, and advanced neural architectures, enabling applications like chatbots and translation systems.

Who is it for?
This course is tailored for beginners and intermediate learners who want to enter the world of AI, as well as developers and professionals who wish to expand their technical toolkit with practical, real-world AI skills.

Learning Outcomes:
By the end of PX01 AI, learners will be able to:

• Implement classic AI algorithms in Python.
• Build intelligent agents capable of problem-solving and decision-making.
• Apply probabilistic reasoning in uncertain environments.
• Develop and train machine learning models.
• Understand and apply neural networks for image, text, and structured data.
• Experiment with NLP models for text classification and language understanding.