Learn Quantum Machine Learning by Solving Real-World Problems, Earn QML Certification

What you'll learn

Understand how quantum computers work and what makes them different from classical computers.

Understand how quantum circuits modify the quantum states to perform useful computation.

Discover the transformative benefits of quantum computing for different applications.

Learn the fundamental building blocks of quantum machine learning.

Understand the fundamentals of quantum neural networks and how to optimize their design for real-world applications.

Learn advanced quantum machine learning algorithms for tasks such as regression, classification, image processing, segmentation, and neural network compression.

Apply quantum machine learning algorithms to real-world, state-of-the-art applications.

Gain exclusive access to Ingenii's Python library for visualizing quantum algorithms and optimizing quantum models.

Develop your skills through hands-on exercises, assessments, and projects that put theory into practice.

Over 20 hours of content and hands-on Python exercises.

Requirements

No physics knowledge required.

No quantum computing knowledge required.

Some programming experience needed for hands-on Python exercises.

A basic understanding of machine learning concepts is recommended.

Description

This hands-on introductory course is designed to bridge the gap between classical machine learning and quantum computing, empowering you with the tools, theory, and practical insights to begin your journey into quantum machine learning (QML). Whether you're a curious learner, a data scientist, or a researcher exploring cutting-edge technologies, this course will guide you through the fundamental concepts of QML and how they can be applied to real-world problems.Through a combination of visualizations, interactive exercises, and hands-on assessments, you'll learn how quantum circuits perform computations, explore foundational quantum algorithms, and discover how quantum algorithms can be optimized for real-world applications such as classification, regression, image processing, and segmentation.You'll also gain exclusive access to Ingenii's Python library for visualizing and optimizing quantum algorithms—designed to make quantum development more intuitive and accessible. By the end of the course, you'll have a solid understanding of quantum machine learning fundamentals and the skills to apply them to practical, impactful challenges.Expanding on our original QML Fundamentals and Medical Imaging courses, and inspired by the learning methods in our upcoming Quantum Hub development resource, this Udemy course combines six, in-depth, application-focused chapters into a complete introductory QML course.Join over 600 data scientists, students, and educators who have already started their Quantum Machine Learning journey.

Overview

Section 1: Introduction to Quantum Machine Learning: Key Concepts & Applications (20+ Mins)

Lecture 1 Introduction

Lecture 2 Why quantum?

Lecture 3 What is a quantum computer?

Lecture 4 The power of qubits.

Lecture 5 Classical and quantum computers solve different problems. Part I

Lecture 6 Classical and quantum computers solve different problems. Part II

Lecture 7 Applications of quantum computing

Lecture 8 Timelines for quantum computing. Part I

Lecture 9 Timelines for quantum computing. Part II

Section 2: Understanding Qubits: The Building Blocks of Quantum Computing (90+ Mins)

Lecture 10 Introduction

Lecture 11 Bits

Lecture 12 Qubits. Part I

Lecture 13 Qubits. Part II

Lecture 14 Measurements. Part I

Lecture 15 Measurements. Part II

Lecture 16 Superposition. Part I

Lecture 17 Superposition. Part II

Lecture 18 Entanglement. Part I

Lecture 19 Entanglement. Part II

Lecture 20 Visualizing multiple-qubit states

Section 3: Quantum Circuits Explained: Designing and Running Quantum Algorithms (150+ Mins)

Lecture 21 Introduction

Lecture 22 Quantum computing paradigms

Lecture 23 Where do I run my quantum algorithms?

Lecture 24 Circuit visualizations

Lecture 25 The X gate. Part I

Lecture 26 The X gate. Part II

Lecture 27 The Z gate. Part I

Lecture 28 The Z gate. Part II

Lecture 29 The Y gate. Part I

Lecture 30 The Y gate. Part II

Lecture 31 The Hadamard gate. Part I

Lecture 32 The Hadamard gate. Part II

Lecture 33 Arbitrary Rotations. Part I

Lecture 34 Arbitrary Rotations. Part II

Lecture 35 Controlled Operations and the CNOT Gate. Part I

Lecture 36 Controlled Operations and the CNOT Gate. Part II

Lecture 37 Complexity of quantum circuits

Lecture 38 Your first quantum algorithm

Section 4: QNNs & The Future of AI/ML (180+ Mins)

Lecture 39 Introduction

Lecture 40 What is QML?

Lecture 41 Transformative benefits of QML

Lecture 42 Future advantages of QML

Lecture 43 Current advantages of QML

Lecture 44 Introduction to Quantum Neural Networks

Lecture 45 Data Encoding

Lecture 46 Variational Circuits

Lecture 47 Optimization Process

Lecture 48 Entangling Capacity & Expressability

Lecture 49 Building a quantum neural network

Section 5: Quantum Image Processing (240+ Mins)

Lecture 50 Introduction

Lecture 51 Unsupervised pipeline for medical imaging segmentation

Lecture 52 Challenges for AI models

Lecture 53 Potential use cases of quantum algorithms

Lecture 54 Quantum Hadamard Edge Detection

Lecture 55 Quantum Reservoirs for Image Processing

Lecture 56 Quantum-Inspired Filters

Section 6: Quantum Image Classification & Segmentation (520+ Mins)

Lecture 57 Introduction

Lecture 58 Image Classification with Tensor Networks

Lecture 59 Neural network compression

Lecture 60 Image Segmentation as a QUBO Problem

Lecture 61 Optimization algorithms for QUBO problems

Curious minds eager to explore the intersection of quantum computing and machine learning.,Scientists and engineers looking to apply quantum concepts to practical, real-world problems.,Data and machine learning scientists interested in adding quantum computing to their skillset.,Innovators and researchers exploring early, impactful applications of quantum technologies across industries.,Learners who are looking for a hands-on, applied introduction to quantum machine learning.