Embark on a comprehensive 12-week journey into the world of AI with our flagship program. Designed for both beginners and intermediate learners, this program provides a structured path to mastering AI fundamentals, machine learning, and practical implementation skills.
Our AI Essentials program offers a carefully structured learning path spanning 12 weeks, with over 160 hours of intensive training. Led by specialized trainers for each module, the program combines theoretical knowledge with hands-on practice, culminating in a real-world capstone project. From Python basics to advanced ML concepts, each week builds upon the previous learning, ensuring a solid foundation in AI and machine learning.
Expert-Led Training: Each module is conducted by specialized trainers, including AI Fundamentals experts, Data Science specialists, and AI Ethics professionals, ensuring deep domain expertise in every topic.
Progressive Learning Structure: Starting from basic Python programming and advancing to sophisticated ML algorithms and model deployment, the course ensures a natural progression of skills with 8-12 hours of training per topic.
Practical Focus: Includes 20 hours of capstone project development, regular mini-projects, and hands-on practice sessions, with dedicated time for tools like Git, Jupyter Notebooks, and Google Colab.
A. AI and Python Fundamentals (Week 1-2)
• AI Concepts and Applications (8 hours)
• Python Syntax and Data Structures (12 hours)
• Basic Algorithms and Problem-Solving (8 hours)
• Python Practice and Mini-Project (12 hours)
B. Data Manipulation and Analysis (Week 3-4)
• NumPy for Numerical Computing (8 hours)
• Pandas for Data Manipulation (12 hours)
• Data Cleaning and Preprocessing (8 hours)
• Data Analysis Practice (12 hours)
A. Data Visualization and Statistics (Week 5-6)
• Matplotlib for Basic Visualization (8 hours)
• Seaborn for Advanced Visualization (8 hours)
• Descriptive Statistics and Probability Basics (16 hours)
• Visualization and Statistics Practice (8 hours)
B. Machine Learning Fundamentals (Week 7-8)
• ML Concepts and Workflow (8 hours)
• Supervised Learning Algorithms (12 hours)
• Unsupervised Learning Basics (8 hours)
• ML Practice and Implementation (12 hours)
A. Model Deployment and Tools (Week 9-10)
• Introduction to Model Deployment (8 hours)
• Version Control with Git (8 hours)
• Jupyter Notebooks (12 hours)
• Google Colab and Cloud Computing (12 hours)
B. Capstone Project and Ethics (Week 11-12)
• Capstone Project Development (20 hours)
• AI Ethics and Responsible AI (8 hours)
• Project Presentations and Course Wrap-up (12 hours)