Basic math knowledge (algebra, statistics).

No prior programming knowledge required.

1. Develop problem-solving skills by identifying and defining real-world problems that can be solved using AI.

2. Build algorithmic thinking through the application of machine learning algorithms, such as linear regression and decision trees, to create simple AI models.

3. Enhance critical thinking by analyzing datasets and selecting the appropriate algorithms to predict outcomes.

4. Improve logical thinking by learning how to evaluate AI models using metrics like accuracy, precision, recall, and F1-score.

5. Apply basic AI tools such as Scikit-Learn and TensorFlow to implement machine learning solutions and automate predictions.

6. Understand the foundations of AI and machine learning and apply these concepts to solve basic classification and regression problems.