Machine Learning Mastery
Build Real-World ML Projects with Python, Algorithms, and Data Science FundamentalsBy Rachel Bennett
Length2h 42m
About this audiobook
Step into the world of intelligent systems and data-driven innovation with Machine Learning Mastery. This practical guide helps you build a strong understanding of Machine Learning, from core concepts to advanced applications used in modern technology.
Designed for both beginners and aspiring practitioners, the book explains how machine learning models work and how they can be applied to real-world problems. With clear explanations and practical insights, you’ll learn how to move beyond theory and begin building smarter systems with confidence.
Here's a Preview of What You'll Learn...
The foundations of data, algorithms, and predictive models
How supervised learning works in practice
Understanding neural networks and deep learning
Feature engineering for better model performance
Ethical considerations and bias in AI systems
Build the knowledge and mindset needed to create intelligent solutions powered by data.
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Audiobook details
GenreTechnology, Business and Economics
Length2 hrs 42 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateMar 8, 2026
LanguageEnglish
Table of contents
1Introduction
10Overfitting, Underfitting, and the Bias-Variance Tradeoff
2Introduction to Machine Learning and Its Real-World Impact
11Hyperparameter Tuning and Model Optimization
3The Foundations: Data, Algorithms, and Models
12Deploying Machine Learning Models in Production
4Supervised Learning Demystified
13Tools, Libraries, and Frameworks Every ML Engineer Should Know
5Diving into Unsupervised Learning
14Ethics and Bias in Machine Learning
Show all chaptersShow less
6The Power of Semi-Supervised and Self-Supervised Learning
15Emerging Trends and the Future of Machine Learning
7Understanding Neural Networks and Deep Learning
16Final Thoughts: Becoming a Machine Learning Master
8Feature Engineering: Turning Data into Gold
17Conclusion
9Model Evaluation and Performance Metrics
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