Master the art of building sophisticated AI systems with our comprehensive 16-week program. Designed for experienced developers and ML engineers, this advanced program covers everything from ensemble methods to MLOps, preparing you to architect and deploy complex AI solutions.
The AI Architect program is an intensive 16-week journey into advanced AI and ML engineering. With over 240 hours of specialized training, you'll master cutting-edge technologies including deep learning, computer vision, NLP, and MLOps. Each module is led by domain experts, combining theoretical depth with hands-on implementation experience.
Advanced ML and Deep Learning: Master ensemble methods, neural networks, and deep learning frameworks with 48 hours of dedicated practice in TensorFlow and Keras.
Specialized AI Domains: Dive deep into Computer Vision, NLP, and Reinforcement Learning with 24 hours of focused training in each domain.
MLOps and Deployment: Learn professional-grade deployment practices including Docker containerization, CI/CD pipelines, and distributed computing with Spark.
Big Data Integration: Master big data processing and ML pipelines with 32 hours of hands-on practice in distributed computing and Spark.
Intensive Project Work: Culminates in a comprehensive 35-hour capstone project, including system design, implementation, and optimization phases.
Professional Development: Regular practice sessions totaling 96 hours across different modules ensure practical mastery of concepts.
A. Advanced Machine Learning (Week 1-2)
• Ensemble Methods (8 hours)
• Feature Engineering (12 hours)
• Model Evaluation and Tuning (8 hours)
• Advanced ML Practice (12 hours)
B. Deep Learning Fundamentals (Week 3-4)
• Neural Network Basics (8 hours)
• TensorFlow and Keras (12 hours)
• Convolutional Neural Networks (8 hours)
• Deep Learning Practice (12 hours)
A. Natural Language Processing and Computer Vision (Week 5-8)
• Text Preprocessing and Word Embeddings (16 hours)
• Sequence Models and Transformers (24 hours)
• Image Processing and Object Detection (20 hours)
• Transfer Learning in CV (20 hours)
B. Reinforcement Learning and MLOps (Week 9-12)
• RL Fundamentals and Q-learning (20 hours)
• Policy Gradient Methods (20 hours)
• Model Versioning and Docker (16 hours)
• CI/CD for ML Projects (24 hours)
A. Big Data Processing (Week 13-14)
• Distributed Computing Basics (8 hours)
• Spark for Big Data Processing (12 hours)
• Big Data ML Pipelines (8 hours)
• Big Data Practice (12 hours)
B. Capstone Project (Week 15-16)
• Project Planning and Design (8 hours)
• Implementation and Development (20 hours)
• Testing and Optimization (8 hours)
• Project Presentation (4 hours)
