Artificial Intelligence for Quantum Machine Learning
A Hands-On Guide from Beginner to Advanced – Unlock the Power of Quantum Computing with Practical Python ProjectsBy Theo Riven
Length4h 25m
About this audiobook
Discover the future of intelligent computing through the powerful combination of artificial intelligence, machine learning, and quantum technology.
Artificial Intelligence for Quantum Machine Learning is a structured guide for readers who want to understand how AI and quantum computing work together to solve complex problems. Starting with the foundations of artificial intelligence and classical machine learning, this book introduces key concepts such as supervised learning, unsupervised learning, reinforcement learning, data preprocessing, model training, and Python-based machine learning tools.
Readers are then guided into quantum computing fundamentals, including quantum bits, gates, circuits, algorithms, and quantum-enhanced learning models. With practical explanations and project-based learning, the book helps bridge the gap between advanced theory and real-world implementation.
Perfect for AI learners, Python programmers, data science students, researchers, and technology professionals, this book offers a clear path into one of the most exciting areas of modern computing.
Audiobook details
GenreTechnology
Length4 hrs 25 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateJun 12, 2026
LanguageEnglish
Table of contents
1Chapter 1: Foundations of Artificial Intelligence and Machine Learning
2Introduction: Why Start Here?
3A Brief History of Artificial Intelligence: From Dreams to Reality
4Key Paradigms in Machine Learning: The Building Blocks
5Supervised Learning: Learning with a Teacher
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6Unsupervised Learning: Discovering Hidden Patterns
7Reinforcement Learning: Learning Through Trial and Error
8Setting Up Essential Python Tools: Your ML Toolkit
9Why These Tools?
10Installation and Environment Setup
11Quick NumPy Primer
12Pandas: Data Wrangling Basics
13Scikit-learn: Your Model Factory
14Data Preprocessing and Exploratory Analysis: Cleaning Before Cooking
15Why Preprocess?
16Step 1: Loading and Inspecting Data
17Step 2: Handling Missing Values and Outliers
18Step 3: Feature Scaling and Encoding
19Step 4: Feature Engineering and Selection
20Building Your First ML Model: Linear Regression in Action
21Understanding Linear Regression
22Step-by-Step Implementation
23Summary: Key Takeaways and Next Steps
24Chapter 2: Core Classical Machine Learning Algorithms
25Introduction: From Basics to Building Blocks
26Supervised Learning: Making Predictions from Labeled Examples
27Decision Trees: Branching Decisions Like a Flowchart
28Random Forests: Strength in Numbers
29Support Vector Machines: Drawing the Line in High Dimensions
30Unsupervised Learning: Finding Structure in the Chaos
31K-Means Clustering: Forming Natural Groups
32Principal Component Analysis (PCA): Compressing the Essence
33Model Evaluation Metrics and Overfitting Prevention: Building Reliable Models
34Key Metrics: Beyond Accuracy
35Overfitting Prevention: Generalize, Don't Memorize
36Hands-On: Train and Tune a Classification Model on Real Datasets
37Step 1: Load and Preprocess
38Step 2: Train Baseline Models
39Step 3: Tune with Grid Search
40Step 4: Evaluate and Compare
41Summary: Key Takeaways and Next Steps
42Chapter 3: Introduction to Quantum Computing: The Basics
43Introduction: Bridging Classical ML to Quantum Frontiers
44Why Quantum Computing Revolutionizes AI: Speedups and New Possibilities
45Core Reasons for the Revolution
46Real-World Applications in AI
47Classical vs. Quantum Bits: From Bits to Qubits
48Classical Bits: The Binary Backbone
49Qubits: Superposition and the Probability Party
50Measurement: The Reality Check
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