Applied Quantum Machine Learning using Qiskit

No PhD in physics required — just ML fundamentals and Python. In 10 live sessions, you'll build QSVMs and QNNs on real datasets, including leukemia, breast cancer, iris, and Yahoo financial data.

Quantum computing is moving from research labs into industry roadmaps. Companies including IBM, Google, and major financial institutions are investing heavily in quantum technologies, creating demand for ML engineers who can understand, evaluate, and build quantum-enhanced machine learning workflows. After the course, you will be able to:

✅ Encode classical data into quantum circuits

✅ Build Quantum Support Vector Machines on real datasets

✅ Train Quantum Neural Networks for classification tasks

✅ Use quantum kernels and clustering techniques

✅ Solve optimization problems with QAOA

✅ Incorporate quantum noise into ML workflows

✅ Understand training challenges such as barren plateaus

✅ Assess when QML may provide an advantage

If you already understand machine learning and want to become quantum-ready before the field reaches wider adoption, this course provides a structured path from fundamentals to practical implementation. This course assumes no background in physics.