AI Engineering - Specialist
The course equips learners with advanced neural networks, production AI systems, MLOps practices, and enterprise-grade model deployment
- Length
6 Months
- Internship
2 Months
- Certificate
Yes
- Level
Specialist
About the course
AI Engineering-Specialist is an advanced, hands-on program designed for learners who want to deepen their expertise in artificial intelligence and build production-ready AI systems. The course explores advanced machine learning, deep learning architectures, generative AI applications, model optimization, AI deployment, and automation workflows. Learners work with real-world datasets and industry-standard tools to design intelligent solutions that solve business and operational challenges. Through intensive practical projects and advanced AI implementation techniques, students develop the technical experience needed for specialized AI engineering and automation roles.
Course Curriculum
MODULE 1 — Full Python for AI Engineering (Advanced)
- Advanced OOP
- Decorators & generators
- Working with APIs
- Logging & error handling
- Python for automation
- Writing production-ready Python scripts
MODULE 2 — Advanced Artificial Intelligence Mathematics
- Vector spaces
- Norms & distance metrics
- Optimization algorithms (SGD, Adam, RMSProp)
- Regularization techniques
- Understanding backpropagation deeply
- Applied math projects for real problems
MODULE 3 — Deep Learning (Advanced)
- Building deep neural networks
- Regularization (dropout, batch norm)
- CNNs in-depth
- Transfer learning
- Model fine-tuning
- Advanced model evaluation
- Deep learning project #1 (vision or NLP)
MODULE 4 — Natural Language Processing (Advanced)
- Advanced text preprocessing
- Word embeddings (Word2Vec, GloVe)
- RNNs, LSTMs, GRUs
- Attention mechanisms (intro)
- Text classification & sequence modelling
- NLP project
MODULE 5 — Computer Vision Engineering (Advanced)
- Convolution operations (deep dive)
- Popular architectures: Res Net, Mobile Net, Efficient Net
- Image segmentation basics
- Object detection (YOLO, SSD introduction)
- Data augmentation techniques
- Computer vision project
MODULE 6 — Generative AI (Foundations)
- What are Generative Models?
- Autoencoders
- Variational Autoencoders (VAEs)
- Introduction to GANs
- Practical generative AI applications
MODULE 7 — MLOps & Model Deployment (Industry-Focused)
- Saving and exporting AI models
- Model packaging
- Flask & FastAPI for AI deployment
- Docker fundamentals
- Monitoring AI models
- CI/CD pipeline introduction
- Deployment project
MODULE 8 — Cloud AI Engineering
- AWS/GCP/Azure AI tools
- Cloud model hosting (SageMaker intro, Vertex AI)
- Managing scalable AI workflows
- Data pipelines for AI
- Cloud compute scaling (GPUs & accelerators
MODULE 9 — Advanced Applied AI Systems
- Recommender systems (intro)
- Time-series forecasting with DL
- Fraud/Anomaly detection
- Real-world AI architectures
- Ethical AI, fairness & bias
MODULE 10 — AI Engineering Capstone Project (End-to-End System)
- Problem framing
- Data engineering pipeline
- Model development (vision/NLP/tabular)
- Deployment using API or cloud platform
- Technical + business presentation
- Portfolio-ready submission
Course Structure
- Saturday Live Classes for 6 months
- Weekly Drop-In Support Sessions
- Mentorship Sessions
- LinkedIn Optimization
- Interview Preparation
- Post-Training Support
- Upwork Optimization
- CV Review
- Navigating the Job Market Session
- 2-Month Internship
- Portfolio Building
- Industry-focused mock interviews
- Job readiness coaching
Emmanuel Chisom Ani
AI Engineering facilitator
Emmanuel Ani is an AI Engineer with a strong passion for building intelligent systems that solve real-world problems. He specializes in leveraging data, machine learning, and modern technologies to develop scalable and efficient solutions.
With a growing expertise in artificial intelligence and data-driven systems, Emmanuel is committed to continuous learning and innovation. He is focused on using AI to drive impact, improve decision-making, and contribute to the advancement of technology across industries.
