Courses

Join our 6-week Introduction to Python and Machine Learning course to gain a solid foundation in programming and key machine learning concepts. Through hands-on projects and practical exercises, you'll develop core skills in Python, data analysis, and introductory machine learning techniques. Ideal for beginners, this course prepares you to take the next steps in AI and data science or advance to more specialized machine learning studies.

Join our 24-week AI and data science bootcamp for hands-on learning in core concepts, tools, and techniques. Gain practical skills in data analysis, machine learning, and AI model development through real-world projects. This program is designed to help you upskill or pivot your career, providing a solid foundation for success.

Transform business data into predictive insight in just 12 weeks. This live cohort drills deep into advanced SQL, feature engineering, statistical modelling, and machine-learning for decision-making, then elevates your storytelling with executive-ready dashboards. You’ll finish by shipping a real-client capstone—data pipeline to deployed model—giving you the GitHub portfolio and interview narratives recruiters look for in Data Scientist, BI Analyst, and AI-focused analyst roles.

Engineer cloud-scale pipelines that keep analytics teams running. Starting with modern ELT in dbt and star/ vault schemas, you’ll master Snowflake, BigQuery and Delta Lake storage, optimise Spark and Databricks for terabyte workloads, and build exactly-once streaming with Kafka and ksqlDB. The capstone delivers a production-grade pipeline plus lineage, cost dashboards and Terraform IaC—proof you’re ready for Data or Analytics Engineering roles in AWS, Azure or GCP environments.

Bridge the gap between notebooks and revenue-generating AI services. Over 12 weeks you’ll refactor ML code into modular packages, fine-tune transformers for Gen-AI features, and deploy FastAPI micro-services in Docker and Kubernetes with autoscaling. MLflow tracking, LangChain RAG pipelines and KEDA cost controls round out the stack. Graduates leave with a live, documented API and the skills employers demand for AI Engineer, ML Engineer or MLOps-heavy product teams.

Ship models that stay healthy long after launch. This cohort covers reproducible pipelines with DVC and Feature Stores, multi-model serving via FastAPI, TorchServe and Triton, and GitHub-Actions CI/CD that pushes blue-green releases to KServe. Observability with Prometheus, drift alerts using Evidently, and rollback playbooks prepare you for real-world incidents. Complete the industry capstone and walk into interviews as an ML Engineer, MLOps Engineer or Model Deployment Specialist.

Go beyond the notebook and put vision models where they matter: in the factory, on the drone, at the edge. Starting with dataset curation and YOLO-v8 fine-tuning, you’ll compress models with ONNX/TensorRT, serve them through NVIDIA Triton, and deploy to Jetson or KServe with auto-scaling GPUs. The capstone—an end-to-end detection or segmentation system—gives you portfolio proof for Computer-Vision or Edge-AI roles in manufacturing, mining, retail, and robotics.

Turn terabytes of documents into instant, trustworthy answers. You’ll master embeddings, hybrid BM25 + vector ranking, and build multilingual RAG pipelines with pgvector, Pinecone, LangChain and OpenAI. Automated hallucination tests, governance dashboards, and Prometheus monitoring ensure your search or Q&A service stays accurate, compliant, and fast. Ideal for engineers modernising legacy Lucene stacks or delivering AI-powered knowledge bases in finance, legal, or energy sectors.

Unlock the full power of large-language models while meeting enterprise-grade security and compliance. In this 12-week cohort you’ll design and deploy Retrieval-Augmented Generation (RAG) services on Kubernetes, wire in bias- and PII-guard-rails, and build cost dashboards that keep CFOs happy. You’ll graduate with a live KServe endpoint, an MLflow-tracked fine-tune, and a governance playbook—exactly what hiring managers expect from a modern Generative-AI or LLM Engineer.

Think like a Staff Engineer. Over 12 intensive weeks you’ll translate product goals into SLIs/SLOs, draft cost-aware architecture diagrams, and implement blue-green and canary roll-outs with Argo Rollouts. From feature stores and data lineage to drift dashboards and GDPR audit trails, you’ll learn the frameworks that underpin reliable, scalable machine-learning in production—then prove it with a peer-reviewed design doc and monitored service.

Deliver insights in under five seconds. This course takes you from change-data-capture with Debezium and Kafka to stateful stream processing in Apache Flink and Spark Structured Streaming. You’ll land data in a Delta Lakehouse, serve online features for ML, and wrap the entire pipeline with Prometheus observability and Great Expectations data contracts. Perfect for engineers facing IoT, click-stream, or telemetry workloads who need exactly-once guarantees at cloud scale.