Ph.D in Applied Math, Coaching Data/AI

Dr. Sharon-Y Veerayah-McGregor

 Train SQL, PowerBI, Python -Data Science, AI, Claude; Engineering Math and GQE

Real Analysis Graduate Qualifying Exam (Math)

Focus on Real Analysis, how to master and prep for GQE

MATH Qualifying Exam for Graduate Students

You will learn Data Encodings, Quantum SVMs, Quantum neural networks, Quantum k-means, and hybrid quantum-classical models in Qiskit 2.5.

Tier-2 IBM Qiskit Advocate 

Abdus Salam International Centre for Theoretical Physics

Penn State College of Engineering

Ayass BioScience

Qiskit

LUMS

Dr. Muhammad Faryad

Tier-2 IBM Qiskit Advocate, Qiskit 2.x Certified Developer

Cohort 3

Cohort 4

‼️Early bird 30% off right now. <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/faryad/aqml?promoCode=EQML">Use Code EQML</a>.‼️Developing countries get 70% off in Early-bird period. <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/faryad/aqml?promoCode=DQML">Use Code DQML</a>.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.

Go from ML to QML in 4 weeks, and showcase it in final capstone project

Master the quantum foundations of QML — no physics background needed

Encode classical data into quantum circuits

Build quantum SVMs and quantum kernels on real datasets

Train quantum neural networks for classification

This course assumes knowledge and fluency with basic classical ML concepts, including neural networks, supervised and unsupervised ML.

Classical Machine Learning Concepts 

Students with prior experience in ML programming and familiar with Python tools will find the course most useful.

Working Knowledge of Numpy, Sklearn, Pandas

Working experience with matrices is required. Exposure to linear algebra before the course will help you dig deeper.

Familiarity with Matrices

AI/ML Engineers &amp; 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.

Build your first quantum finance model with QAOA on real IBM quantum hardware

Tier-2 IBM Qiskit Advocate, IBM-certified Qiskit developer

Cohort 2

‼️60% off till 23 September <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/faryad/qaoa?promoCode=EEB">(code EEB)</a>🌍70% off for developing countries: <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/faryad/qaoa?promoCode=EB">(code EB)</a>Quantum optimization is the most commercially scrutinized corner of quantum computing, yet most engineers who want to assess it face a gap: tutorials are either toy demos with no real data or papers that assume a physics degree. This workshop closes that gap in one sitting. In 2.5 hours, you will derive QAOA from first principles, build the complete pipeline on real market data (10 and 6 US stocks, actual return and covariance figures), run it in Qiskit 2.4, and benchmark it against the exact classical optimum.What you will get:⭐ Full QAOA in 2.5 hours — theoretical foundations and Qiskit codes⭐ Four Jupyter notebooks — Yahoo data fetching, Qiskit Primitives, QAOA on simulator for 10 stocks, and on IBM hardware for six stocks⭐ IBM Hardware — Custom optimization routine to run on hardware in a series of single jobs through open plan⭐ All PDF slides — complete derivations written to be a standalone reference⭐ Full recording — Full recording available in two hours⚛️ "Do I need a paid IBM account?" No. The custom optimizer has been specifically developed to run on the free IBM Open Plan in 2 minutes.

Master the full QAOA pipeline on real market data — and become the person your team trusts to assess quantum optimization.

Formulate a real financial problem for a quantum computer

Understand QAOA from first principles, not as a black box

Run the complete hybrid quantum–classical pipeline in Qiskit 2.4

Run on actual IBM quantum computer

ML engineer at a bank tasked with evaluating quantum optimization — strong ML background, no QC experience, want your own honest benchmark.

Physicist or QC researcher who knows the theory — want to see a complete, honestly-benchmarked real-world QML application, end-to-end.

Risk manager or CTO asked to evaluate quantum computing for your firm — technical enough to judge, tired of slick marketing and hype.

Perform & lead confidently under pressure. Build skills for adaptability, resilience, and sustainable high performance.

ex-Amazon Chief EQ Evangelist

Amazon Web Services

Oracle

Johnson & Johnson

Capital One

Cisco

Rich Hua

Former Chief EQ Evangelist at Amazon. Scaled EQ to 1.5 million people.

AI is raising the bar for performance and leadership. Priorities shift constantly, answers are incomplete, and leaders must move more quickly than ever. Teams don't underperform because they lack IQ but because of human challenges from uncertainty, pressure, and fatigue.Through this course, you will:• Strengthen emotional agility to think clearly in high-stakes situations• Increase adaptability and resilience (for self and team) to sustain high performance without burning out• Transform disagreements into better decisions through productive conflict• Lead with high expectations &amp; high empathy to bring out the best in others• Deepen trust and connection in remote/hybrid work environments• Influence managers &amp; teammates to achieve your goalsYou will apply skills to Moments That Matter (MTM), creating a playbook with scripts you can use immediately. Expect live practice, real scenarios, and personalized feedback.Whether you're a people manager or senior IC, this course will give you the mindsets and skill sets to optimize your performance—and that of your team—in the AI era.Rich was recently featured in Forbes for his pioneering work: <a target="_blank" rel="noopener noreferrer nofollow" href="http://bit.ly/4rIbN6k">http://bit.ly/4rIbN6k</a>

EQ is the critical professional skill in uncertainty & change

Build an emotional recovery plan to sustain performance and prevent burnout

Boost your adaptability and resilience

Increase your social awareness and empathy

Cultivate a fearless and innovative team culture

Engage in generative disagreement to improve decision-making

Become a resonant and inspiring leader

People managers and leaders (heads of, team leads, directors). You run a team and have to get buy-in upwards and cross-functionally.

Senior individual contributors (ICs). Marketing leads, PMs, engineers, sellers, program managers, etc. Minimum 3+ years of experience.

Teams who want high team EQ so you can move faster, raise the bar, deliver and receive feedback effectively, and operate with shared purpose

Go from a demo and a feeling to an evaluation practice you can run every week and defend to whoever controls the budget.

I build AI that has to survive an audit

HashiCorp

VMware

NGINX

University of California

Thomas Underhill

Product & Eng Leader (AWS, VMware, HashiCorp, NGINX) · Adjunct Professor

Cohort 1

Your team put a model in production and it seems to be working. Then someone above you asked what the AI spend is returning, and the honest answer was a demo and a feeling.That is not a failure of rigor on your part. It is a structural problem with a name. On complex work, checking an answer costs about as much as producing it, so grading does not scale, so nobody grades. Ninety six percent of engineering teams use AI tools. Around twenty percent measure the result specifically.The gap is not laziness. It is that the standard advice does not work on the problems you have. Accuracy misleads on imbalanced data. Your eval set is probably contaminated and you cannot check. And for the most valuable work your system does there is no right answer to compare against, which is where most teams give up and let a model grade a model.There are ways out of all of that, and none is new. Metamorphic testing dates to 1998 and was never pointed at language models until recently.This course builds one evaluation practice, for one real system of yours, across five weeks. You leave able to say what working means, prove whether you have it, and defend the number to whoever holds the budget.

You shipped it. Now somebody wants a number, and you do not have one.

Define what working means for output that changes every run

Build an evaluation set you can actually trust

Choose a metric you can defend, and set the threshold from cost

Grade the work that has no right answer to compare against

Catch a regression before a customer does

Defend the whole thing to the person holding the budget

Every exercise runs against a real system of yours. Without one there is nothing to evaluate, and the coursework does not work in theory.

An AI system already in production, or about to be

The exercises run on my data, but the plan is for your system. You need to know what it does, what failure costs, and who cares about it.

A system you are accountable for, and enough context to describe it

Every exercise is a design decision rather than an implementation. You will read code. Your engineers build what you specify.

No machine learning background, and no code required

Engineering leader who shipped it and cannot measure it.

Product leader being asked what the AI spend returns

Anyone whose team grades model output with another model

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

AI for GTM

Sales

Customer Success

GTM Engineering

Everyone

Founders

Operators

Data Scientists

Business Analysts

User Researchers

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Broad professional audience — content relevant across many roles and functions.

Real Analysis Graduate Qualifying Exam (Math)

What you'll learn

Why this topic matters

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Dr. Sharon-Y Veerayah-McGregor