Artificial Intelligence (AI) Course

Differentiate AI concepts and categories.

Understand the purpose and classification of search algorithms.

Breadth-First Search & Depth-First Search

Uniform Cost Search

Learn heuristic-based searching.

Select appropriate search algorithms for different problems.

Python for AI

Operators & Conditional Statements

Organize and reuse Python code effectively.

Organize and work with collections of data in Python.

Work with files and manage runtime errors in Python.

Apply Python programming concepts in a practical project.

Prepare data for AI models.

Understand the purpose and use of major Python libraries in AI.

Visualize data effectively using common chart types.

Understand the fundamentals and workflow of Machine Learning.

Learn the fundamental concepts of supervised learning.

Understand the Linear Regression algorithm.

Understand the Logistic Regression algorithm.

Implement Linear and Logistic Regression models on real datasets.

Learn the Decision Tree algorithm.

Practical Lab for Decision Tree & Random Forest

Explore the K-Nearest Neighbors classification algorithm.

Explore the Naive Bayes classification algorithm.

Explore the Support Vector Machine classification algorithm.

Implement KNN, Naive Bayes, and SVM on real datasets.

Evaluate AI model performance using common evaluation metrics.

Understand unsupervised learning techniques using clustering and K-Means.

Understand PCA as an unsupervised learning technique.

Learn how AI learns through interaction with an environment.

Demonstrate knowledge and skills through a final project, assessment, and course review.