Course Syllabus & Curriculum
A comprehensive 12-week program covering all essential aspects of artificial intelligence, from fundamentals to advanced applications.
Prerequisites
Required
- Basic programming knowledge (Python preferred)
- Understanding of mathematics (linear algebra, calculus, statistics)
- Familiarity with data structures and algorithms
- Access to a computer with at least 8GB RAM
Recommended
- Previous experience with Jupyter Notebooks
- Basic understanding of probability theory
- Familiarity with Git and version control
- Experience with data analysis or visualization
Course Structure
A balanced mix of theory and practice with approximately 40-45 hours of commitment per week.
Live Sessions
Interactive lectures with Q&A
Hands-On Labs
Practical coding exercises and projects
Self-Study
Reading assignments and video lectures
Group Projects
Collaborative team-based assignments
Week-by-Week Curriculum
Detailed breakdown of topics, learning objectives, and projects for each week of the program.
Topics Covered:
- History and evolution of artificial intelligence
- Types of AI: Narrow AI, General AI, and Super AI
- Python programming essentials for AI
- NumPy and pandas for data manipulation
- Matplotlib and seaborn for data visualization
Learning Objectives:
- Understand the fundamentals of AI and its applications
- Set up Python development environment
- Perform basic data manipulation and analysis
Project:
Exploratory Data Analysis on a real-world dataset
Assessment & Grading
Your performance will be evaluated through multiple assessment methods to ensure comprehensive understanding.
Weekly Assignments
30%Coding exercises and problem sets to reinforce learning
Group Projects
25%Collaborative team projects building real-world applications
Midterm Assessment
15%Comprehensive evaluation of fundamental concepts (Week 6)
Capstone Project
30%Final project demonstrating mastery of AI concepts
Grading Scale
A minimum grade of C (70%) is required to receive the certificate