Overview
This course introduces learners to Python programming with a focus on Artificial Intelligence applications. It covers essential Python concepts, libraries, and techniques used in AI, including data handling, automation, and basic machine learning integration.
What You’ll Learn
- Core Python programming for AI development
- Data manipulation with NumPy and Pandas
- Using Scikit-learn for basic machine learning tasks
Skills You’ll Gain
- Writing clean, efficient Python code
- Data preprocessing, transformation, and analysis
- Implementing simple AI models and automation workflows
Prerequisite: None
Course Contents:
Introduction to Python syntax, data types, and control structures; Functions, loops, and file handling; Working with NumPy arrays and Pandas DataFrames; Introduction to Matplotlib for basic visualizations; Using Scikit-learn for classification and regression tasks; Automating AI workflows; Building a mini AI project (e.g., sentiment analysis or spam detection); Debugging, testing, and best practices for clean code.
Teaching Methodology:
Interactive coding labs, project-based learning, live coding sessions, and assessments.
Reference Material:
- Python Crash Course by Eric Matthes
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- Python official documentation and tutorials
- Real-world datasets from Kaggle and UCI Machine Learning Repository