Go beyond basic ML and build production-ready AI systems. Master Scikit-Learn, TensorFlow, and PyTorch for model development — then advance to fine-tuning LLMs, building RAG systems, deploying models with MLOps, and using Claude, ChatGPT, and Hugging Face to accelerate every stage of the ML workflow.
Labs building real models on real datasets — customer churn, image classification, sentiment analysis, and LLM-powered applications. Every lab runs in Jupyter with GPU-enabled cloud instances.
Claude and ChatGPT help prototype models, explain training losses, suggest architectures, and generate evaluation code. Copilot writes Scikit-Learn and PyTorch boilerplate. This is how modern ML teams work.
Not just model training — you deploy models to production, version experiments with MLflow, build CI/CD for ML, and implement model monitoring. Skills that separate ML engineers from ML students.
Train an XGBoost churn model with SHAP explanations, track experiments with MLflow, deploy as a FastAPI service in Docker, and generate AI-written business recommendations from predictions.
Fine-tune ResNet on a custom product image dataset, build a FastAPI serving endpoint with batch prediction support, containerise with Docker, and implement model performance monitoring.
Build a production RAG system that ingests company documentation, indexes with ChromaDB embeddings, answers questions with Claude, and serves via a FastAPI REST API with conversation memory.
Build a PyTorch LSTM model for sales forecasting, implement a full MLOps pipeline with DVC and MLflow, deploy with FastAPI, and add drift monitoring that alerts when distribution shifts.
Build a complete ML experimentation platform where Claude suggests architectures, Copilot writes training loops, MLflow tracks experiments, and Claude generates model performance narratives for stakeholders.
Upon completing all labs and the capstone project, you receive a verified certificate in Machine Learning with Python & AI Tools — covering traditional ML, deep learning, LLMs, RAG systems, and MLOps. Shareable on LinkedIn and portfolio-ready.
Train, evaluate, and deploy machine learning models for production applications at scale.
Build LLM-powered applications — RAG systems, fine-tuned models, and AI-assisted products.
Apply statistical ML to business problems — churn, forecasting, segmentation, and recommendation.
Build CI/CD pipelines for ML, implement experiment tracking, model registries, and serving infrastructure.
Specialise in transformer models, BERT fine-tuning, LangChain applications, and production NLP systems.
Design AI-powered system architectures using LLM APIs, RAG, and cloud ML platforms.
"The RAG module transformed my understanding of LLMs. I built a customer support bot at work that reduced ticket volume by 40%. The combination of LangChain, ChromaDB, and Claude is incredibly powerful."
"The MLOps section is what separates this course from Coursera. I can now deploy models properly — not just notebooks but actual production FastAPI services with monitoring. Got promoted to Senior ML Engineer."
"Claude suggesting PyTorch architectures in real-time during the labs is a productivity multiplier. I prototyped three model architectures in the time it would have taken me to build one manually."
