Build Quantum ML Models on Real Datasets
‼️Early-Bird enrollments: 40% off. Use code Early.
If you understand training loops, kernels, and neural networks, you already know most of what quantum machine learning requires. The concepts map directly: the kernel trick becomes quantum kernels, training loops become variational circuits, neural networks become quantum neural networks. What's missing is the quantum layer — and that is precisely what this course teaches.
What is included:
⭐ 9 live sessions with recordings — theoretical foundations plus guided walkthroughs of the code files, so you see every implementation built line by line
⭐ 10+ Notebooks — complete, end-to-end QML pipelines in Qiskit 2.5, built on real datasets like leukemia, breast cancer, and iris
⭐ All PDF slides — complete derivations and written to be a standalone reference
⭐ Capstone project — Structured and guided final project on applications of QML
🏢 Employer-sponsored: Most learners get reimbursed. Reimbursement request template available on request. A certificate of completion will be issued to all enrolled participants.
What you’ll learn
You will learn Data Encodings, Quantum SVMs, Quantum neural networks, Quantum k-means, and hybrid quantum-classical models in Qiskit 2.5.
States, gates, and measurement taught as linear algebra you already use
Superposition, interference, and entanglement as computational resources
Qiskit 2.4 primitives: SamplerV2 and EstimatorV2 from day one
Basis, amplitude, and angle encoding — and when to use each
Design quantum feature maps as feature engineering
Understand how encoding choices drive model expressivity
The kernel trick, extended to quantum Hilbert spaces
Train QSVMs on leukemia, breast cancer, and iris data
Implement quantum k-means for unsupervised learning
Variational circuits as the quantum analogue of training loops
Choose ansätze, cost functions, and optimizers that actually converge
Diagnose barren plateaus and other trainability failures
Learn directly from Muhammad
Dr. Muhammad Faryad
Tier-2 IBM Qiskit Advocate, Qiskit 2.x Certified Developer
Who this course is for
AI/ML Engineers & Researchers — Who want to implement, benchmark, and evaluate quantum ML models with theoretical depth and practical codes.
Computational Scientists — Who want to expand their computational tools to include practical quantum computing routines for data analysis.
Tech Leads — Who want to develop insights for quantum ML use-cases, hardware limits, and quantum-ready workforce.
Prerequisites
Classical Machine Learning Concepts
This course assumes knowledge and fluency with basic classical ML concepts, including neural networks, supervised and unsupervised ML.
Working Knowledge of Numpy, Sklearn, Pandas
Students with prior experience in ML programming and familiar with Python tools will find the course most useful.
Familiarity with Matrices
Working experience with matrices is required. Exposure to linear algebra before the course will help you dig deeper.
What's included
Live sessions
Learn directly from Dr. Muhammad Faryad in a real-time, interactive format.
Video Recordings
All live sessions will be recorded and made available to students right after each session.
End-to-end code files
All code files used during the live sessions will be delivered to students before showing end-to-end model building for real datasets.
Lecture slides
All lecture slides used during live sessions will be provided to students before each session.
Dedicated Q/A sessions
Live questions/answers sessions and discussion on QML and converting your ML projects to QML projects
Solved Assignments
Theory/coding problems with solutions
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
9 live sessions • 22 lessons
Week 1
Sep
29
Lesson 0 — Orientation: Course Intro, Qiskit Setup, Your First Quantum Circuit Run, Q & A
Sep
30
Lesson 1 — Quantum Computing Foundations: States, Gates, the Bloch Sphere, and Qiskit 2.x
Oct
1
Lesson 2 — Quantum Mechanics, Entanglement, and Qiskit Primitives: Postulates, Interference, Rotation Gates, SamplerV2 & EstimatorV2
Slides
Jupyter Notebooks
Practice Assignment with Solutions
Accessing IBM Hardware
Week 2
Oct
6
Lesson 3 — From Classical Machine Learning to Quantum Models: Risk Minimisation, Observables, the Parameter-Shift Rule, and Data Re-Uploading
Oct
7
Lesson 4 — Quantum Feature Maps: Data Encodings, Entangling Maps, and Quantum Kernels
Slides
Jupyter Notebooks
Free resources
Schedule
Live sessions
3-4 hrs / week
See the syllabus for the detailed schedule.
Tue, Sep 29
4:00 PM—5:15 PM (UTC)
Wed, Sep 30
4:00 PM—5:15 PM (UTC)
Thu, Oct 1
4:00 PM—5:15 PM (UTC)
Playing with Qiskit code files
2-3 hrs / week
You will get the most out of the course if you spend time playing with the Qiskit code files explained during the live lessons.
Reviewing lesson slides
1-2 hrs / week
You should spend time reviewing the lesson slides before and after each live session since the slides have a rigorous treatment of QML theory.
Industry Alumni
AVP Retail & Projects, Valiram Group, Malaysia
Software Engineer, Innovate for Consulting and Technology, Cairo, Egypt
QC Coordinator, Amerisci, New York, USA
Staff Machine Learning Engineer, Chewy, Florida, USA
Actuary, Sompo Insurance, México
Freelance Software Developers, UK, France
Founder, Quantum Computing Company, USA
Research Assistant, Quantum Computing Company, South Korea
Senior AI/ML Engineer, PITB, Pakistan
Software Engineer, HSS, New York
Software Engineer, Kaung Sein Tech, Myanmar
Data Scientist, Acorn Bioenergy, London, UK
Data & AI Engineer, Mueasurebit, Maharashtra, India
Senior Data Scientist, Teradata, Pakistan
Solution Advisor, Omnicell, Texas, USA
Architect, L & T, Telangana, India
Academic Alumni
Lecturer, Northern Border University, Saudi Arabia
Research Assistant, Binghamton University, New York, USA
PhD Student, Queen’s University, Ontario, Canada
Student, IISERM, Mohali, India
Student, Universidad Anáhuac México Norte, México
Associate Professor, LUMS, QAU, Pakistan
Student, LUMS, QAU, Pakistan
Post Doc, Saint Louis University, USA
Student, Canakkale Onsekiz Mart University, Turkey
Lecturer, Aston University, UK
Faculty, University Ibn Zohr, Morocco
Quantum Physics Faculty, Mohammed V University, Morocco
Graduate Student, Riverside, CA, USA
Student, IIT Kottayam, Kerala, India
Informatics researcher, University of Chicago, USA
Frequently asked questions
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