Full Course Description
Artificial Intelligence in 2026. Transition from foundational Machine Learning to building Agentic Workflows, Multimodal RAG systems, and deploying.
Course Overview
- Next Batch:New batches start every month. Limited seats available – enroll early!
- Course Duration:8 Weeks (Flexible schedules with part-time and full-time options).
- Eligibility: Engineering learners IT professionals Data Science Enthusiasts Anyone curious about AI.
- Engineering learners.
- Data Science Enthusiasts.
- Anyone curious about AI.
Artificial Intelligence (AI) Training Course
- Understand the fundamentals and applications of Artificial Intelligence and Machine Learning.
- Learn Python programming for AI algorithm development and data manipulation.
- Implement supervised, unsupervised, and reinforcement learning models.
- Explore Deep Learning, Neural Networks, and Natural Language Processing (NLP).
- Build intelligent AI projects using TensorFlow, Keras, and OpenCV.
- Prepare for the Artificial Intelligence Professional credential preparation assessment and AI-based career roles.
Overview of Artificial Intelligence (AI) Training Course
The Artificial Intelligence (AI) professional learning track is designed for learners, data professionals, and tech enthusiasts who want to understand how machines can think, learn, and adapt. The course combines concepts, coding, and real-world assignment-based learning to help you master real-world AI applications, providing you with the specialized technical expertise needed to advance your career.
Through hands-on projects, learners gain experience in image recognition, predictive modeling, and chatbot creation. By the end of this program, you'll earn your AI credential preparation and be ready to work as an AI Engineer, Data Scientist, or Machine Learning Specialist.
1. Introduction to Artificial Intelligence
- What is AI? Applications and History.
- Building Intelligent Agents (Search, Games, Logic).
2. Programming with Python for AI
- Python Basics for AI.
- Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
3. Statistics & Machine Learning
- Data Preprocessing & Feature Engineering.
- Supervised and Unsupervised Learning.
- Algorithms: Regression, Classification, Clustering.
4. Neural Networks & Deep Learning
- Perceptrons, MLP, and Backpropagation.
- CNN, RNN, and Deep Neural Networks.
6. Computer Vision & Image Processing
- Image Classification and Object Detection.
- Face and Emotion Recognition.
- Image vs Video Processing.