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Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning – the use of quantum computing for the computation of machine learning algorithms.

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## Here is what you will Learn from Hands-On Quantum Machine Learning with Python

**1. Getting Started**

- Introduction
- Quantum Machine Learning—Beyond The Hype
- Quantum Computing
- The Case for Quantum Machine Learning
- Quiz: Introduction to Quantum Machine Learning

**2. Binary Classification**

- Getting and Looking at the Dataset
- Data Preprocessing: Missing Values
- Data Preprocessing: Identifiers
- Data Preprocessing: Handling Text and Categorical Attributes
- Data Preprocessing: 4- Feature Scaling

**3. Qubit and Quantum States**

- The Qubit
- Exploring the Quantum States
- A Visual Exploration of the Qubit State
- Bypassing the Normalization
- Exploring the Observer Effect

**4. Probabilistic Binary Classifier**

- Towards Naïve Bayes
- Bayes’ Theorem
- Gaussian Naïve Bayes
- Quiz: Probabilistic Binary Classifier

**5. Working with Qubits**

- You Don’t Need to Be a Mathematician
- A Reimplementation of OR
- The Measured Qubit
- Quantumic Math
- When to Differentiate State |0> From State |1>
- Gamble with Quantum Computing

**6. Working with Multiple Qubits**

- Hands-On Introduction to Quantum Entanglement
- Implementing the CNOT gate
- The Equation Einstein Could Not Believe
- The Two Qubit States and Their Transformation
- Calculating the Transformation Matrix

**7. Quantum Naïve Bayes**

- More on Naïve Bayes
- Pre-Processing
- PQC
- Calculating the Posterior Probability
- Post-processing

**8. Quantum Computing Is Different**

- The No-Cloning Theorem
- How to Solve a Problem with Quantum Computing
- Depicting the Transformation O-gate
- Deutsch’s Algorithm
- The Quantum Oracle, Demystified

**9. Quantum Bayesian Networks**

- Introduction to Quantum Bayesian Networks
- Bayesian Networks
- Composing Quantum Computing Controls
- The CCNOT-gate
- Circuit Implementation

**10. Bayesian Inference**

- Introduction to Bayesian Inference
- Estimating a Single Data Point
- Calculating the log‐likelihood when ignoring the missing data
- Estimating a Variable

**11. The World Is Not a Disk**

- The Qubit Phase
- Two Different Qubit States
- The Different States in the Bloch Sphere
- Visualizing the Invisible Qubit Phase

**12. Working with the Qubit Phase**

- Using Grover’s Algorithm
- Basic Amplitude Amplification
- Two-qubit Amplification
- The Two‐qubit Grover Searching |10>
- Quiz: Working with the Qubit Phase

**13. Search for Relatives**

- The Convenience Function and Probabilities for Relatives
- Turning the Problem into a Circuit
- The Oracle and Amplifier Functions
- The Search Algorithm
- Multiple Results

**14. Sampling**

- Forward Sampling
- Bayesian Rejection Sampling
- Quantum Rejection Sampling
- The Amplifier Function
- Quiz: Sampling

**16. APPENDIX**

- Configuring Your Quantum Machine Learning Workstation
- Configuring Ubuntu for Quantum Machine Learning with Python
- How to Set Up JupyterLab for Quantum Computing on windows