Download Quantum Machine Learning Course with Python 2025
Quantum Machine Learning Course with Python, Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), Quantum Support Vector Machines (QSVMs). This course is designed to introduce you to the rapidly growing field of Quantum Machine Learning (QML) — a fusion of quantum computing principles with powerful machine learning techniques. By the end of this course, you will be equipped with the knowledge and skills to build and experiment with quantum-enhanced models using Python and cutting-edge frameworks.
What you’ll learn
- Pennylane
- Introduction to quantum feature map
- Introduction to Quantum Data Encoding
- Quantum Kernel Method
- Introduction to Quantum Clustering algorithm
- Implementation of Quantum Fuzzy Clustering
- Introduction to Quantum Support Vector Machine
- Introduction to Variational Quantum Classifier (VQC)
- What is Quantum K-Means Clustering
- 3D Optimized Quantum k-Means
- Quantum deep learning
- Quantum Neural Networks (QNN)
- Quantum Convolutional Neural Networks
- QGAN
- QGAN implementation with 3-D data
- Quantum Transfer Learning
Course content
- How to make the most of this course
- Tools used in this course
- Introduction to Quantum machine learning (QML)
- Why do we need to use Quantum Machine Learning
- Basic concepts of machine learning
- Introduction to pandas
- Introduction to numpy
- Introduction to matplotlib
- Basics of supervised, unsupervised, and reinforcement
- Neural network and optimization in machine learning
- Classical Kernel Methods and Support Vector Machines
- Simple SVM implementation
- Introduction to PennyLane
- Simple implementation of PennyLane
- Introduction to PyTorch
- Introduction to quantum feature map
- Quantum feature map implementation
- Introduction to Quantum Data Encoding
- Implementation of Basic and Amplitude Encoding
- Code explanation for Quantum Feature Map Implementation
- Introduction to Quantum Kernel Method
- Quantum Kernel Method implementation
- Code explanation for Quantum Kernel Method implementation
- Introduction to Quantum Clustering algorithm
- Introduction to Quantum Fuzzy Clustering
- Implementation of Quantum Fuzzy Clustering
- Segment an image into regions using Quantum kernel-based fuzzy clusterin
- Introduction to Quantum Support Vector Machine
- Implementation of quantum support vector machine
- Code explanation for quantum support vector machine implementation
- Introduction to Variational Quantum Classifier (VOC)
- Implementation of Variational Quantum Classifier (VOC)
- Code explanation of Variational Quantum Classifier (VOC)
- What is Quantum K-Means Clustering
- Quantum k-Means Clustering Implementation
- Implementation of 3D Quantum k-Means
- Implementation of 3D Optimized Quantum k-Means
- Code explanation for 3D Optimized Quantum k-Means implementation
- Introduction to quantum deep learning
- Implementation of quantum deep learning with PennyLane
- Code explaination for quantum deep learning
- What is Quantum Neural Networks (ONN)
- Simple Implementation of Quantum Neural Network
- Code explanation for Quantum Neural network
- What is Quantum Convolutional Neural Networks
- Simple implementation of quantum Convolutional Neural networks
- Code explanation for quantum Convolutional Neural Networks
- Introduction to OGAN
- OGAN Implementation
- Code explanation for OGAN Implementation
- OGAN implementation with 2-D data
- OGAN implementation with 3-D data
- Code explanation for OGAN implementation with 3-D data
- Introduction to Quantum transfer Learning
- Implementation of Quantum transfer learning
- Explanation for Simple Quantum transfer learning implementation
- Quantum Transfer Learning implementation with ResNet + PennyLane
- Code explanation for Quantum Transfer Learning implementation with ResNet
Who this course is for:
- Anyone who wants to learn about quantum machine learning
- Anyone who wants to improve python
- Anyone who wants to become quantum machine learning engineers
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Last updated 9/2025
- Video duration: 8h 10m
- Number of lessons: 4 sections, 60 lectures
- Language: Language: English
- Compressed file size: 3.3 GB
