Module 1: Applied Data Analysis & Classical Machine Learning
30 learning hours
- NumPy, Pandas, Feature Engineering
- Regression, Classification, Tree Models
- Model Evaluation Metrics
Applied project: Predictive customer churn model with explainable AI.
Cloud Education Learning · Program Guide
AI & Machine Learning Engineering
CEL-AIM-404 · 18 Weeks (140 Hrs) · Live Online + GPU Sandbox
Build intelligent systems with Python, PyTorch, Scikit-Learn, Deep Learning, and Generative AI Large Language Models.
About the program
Step into the future of enterprise intelligence. Learn foundational mathematics, statistical modeling, computer vision, natural language processing, and prompt engineering/RAG systems.
Program highlights
- Supervised & Unsupervised Machine Learning
- Deep Learning with PyTorch & Neural Networks
- NLP, Transformers & HuggingFace Models
- Generative AI & Retrieval-Augmented Generation (RAG)
- MLOps & Model Deployment with FastAPI
Curriculum
Module 2: Deep Learning & Neural Networks with PyTorch
40 learning hours
- Backpropagation, Optimizers, Loss Functions
- CNNs for Computer Vision
- RNNs & LSTMs for Sequence Data
Applied project: Medical image classification with transfer learning.
Module 3: Transformers, NLP & Generative AI
40 learning hours
- Attention Mechanism & Transformer Architecture
- HuggingFace pipelines
- Building RAG systems with Vector DBs
Applied project: Enterprise document question-answering AI assistant.
Module 4: MLOps & Scalable Model Serving
30 learning hours
- FastAPI model inference endpoints
- Dockerizing ML models
- Monitoring model drift & retraining
Applied project: Real-time AI recommendation API with automated CI/CD.
Learning outcomes
- Train, fine-tune, and evaluate deep learning and NLP models.
- Build production RAG pipelines utilizing modern vector databases.
- Deploy ML models as low-latency microservices.
Program details are subject to change. Subject to authorized partnership status and approved branding.