AI+ Architect™
The AI+ Architect certification offers comprehensive training in advanced neural network techniques and architectures. It covers the fundamentals of neural networks, optimization strategies, and specialized architectures for natural language processing (NLP) and computer vision. Participants will learn about model evaluation, performance metrics, and the infrastructure required for AI deployment. The course emphasizes ethical considerations and responsible AI design, alongside exploring cutting-edge generative AI models and research-based AI design methodologies. A capstone project and course review consolidate learning, ensuring participants can apply their skills effectively in real-world scenarios. This certification equips learners with the knowledge and practical experience to excel in AI architecture and development.
Fee structure:
Self-Learning Mode:
Each certificate: INR: 19,999
(online videos access + practice tests) – 4 weeks
On-line Instructure led training:
Certificate: INR: 29,999
(online videos access + practice tests) – 4 weeks
Objectives
End-to-End AI Solution Development
Learners will be able to develop end-to-end AI solutions, encompassing the entire workflow from data preprocessing and model building to deployment and monitoring. This includes integrating AI models into larger systems and applications, ensuring they work seamlessly within existing infrastructures.
Neural Network Implementation
Learners will gain hands-on experience in implementing various neural network architectures from scratch using programming frameworks like TensorFlow or PyTorch. This includes creating, training, and debugging models for different applications.
AI Research and Innovation
Learners will be equipped with the ability to conduct AI research, enabling them to stay at the forefront of AI developments. This includes identifying research gaps, proposing novel solutions, and critically evaluating current AI methodologies to drive innovation in the field.
Generative AI and Research-Based AI Design
Learners will explore advanced concepts in generative AI models and engage in research-based AI design. This includes developing innovative AI solutions and understanding the latest advancements in AI research, preparing them for cutting-edge applications and further research opportunities.
Prerequisites
- A foundational knowledge on neural networks, including their optimization and architecture for applications.
- Ability to evaluate models using various performance metrics to ensure accuracy and reliability.
- Willingness to know about AI infrastructure and deployment processes to implement and maintain AI systems effectively.
