Quantum Computing & Machine Learning: Build with Qiskit

Quantum Computing & Machine Learning: Build with Qiskit
Published 8/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch

You understand what a qubit is. Now it’s time to build with one.
This is the intermediate, coding-first course that turns quantum curiosity into real, working code. Over 19 sections and 99 lessons – around 11 hours of video, with 52 hands-on labs – you’ll write and run genuine quantum programs in Python and Qiskit, from your very first Bell state to a complete quantum machine learning capstone.
We don’t just talk about algorithms; we build them. You’ll implement Deutsch-Jozsa, Bernstein-Vazirani, Simon’s, Grover’s search, the Quantum Fourier Transform, phase estimation, and a small run of Shor’s algorithm – line by line, then run them on a simulator and watch the results appear. Every hands-on lab comes with a short companion walkthrough clip showing the exact code, circuit, and output, so nothing stays abstract.
What you’ll build and learn
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A real quantum toolkit.
Set up Python, Jupyter, Qiskit, and PennyLane, and learn the modern Qiskit workflow: build, transpile, verify, run – including on real IBM hardware.
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Gates and circuits from scratch.
Pauli, Hadamard, phase, and rotation gates; multi-qubit entanglers; the Bloch sphere in code; measurement, shots, and reading results.
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The famous algorithms, implemented.
Quantum parallelism and phase kickback, then Deutsch-Jozsa, Bernstein-Vazirani, Simon’s, Grover’s, QFT, phase estimation, and Shor’s – as code you run.
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QAOA for Max-Cut and VQE for the H₂ molecule, plus the optimizers, benchmarking, and NISQ-era reality behind them.
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Error and noise.
Model noise with Qiskit Aer and build the bit-flip, phase-flip, and Shor codes; apply readout-error and ZNE mitigation.
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Quantum machine learning, hands-on.
You’ll also get
a resource-and-quiz sheet with every lesson, plus a companion demo clip for all 52 labs – so you can watch it, then do it yourself.

Who this course is for
• Learners who finished a beginner quantum course (or already know the basics) and want to actually build.
• Python developers and data scientists moving into quantum computing and quantum machine learning.
• Students and researchers who want practical Qiskit and PennyLane skills, not just theory.
Requirements:
comfort with basic Python and high-school math. No prior Qiskit experience needed – we install and set up everything together. A free IBM Quantum account lets you run on real hardware.
By the end, you’ll be able to build, run, and debug quantum circuits and quantum machine learning models with confidence – and you’ll be ready for the Expert course.
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