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Machine Learning Basics: A Practical, No-Math-PhD Guide to Core Concepts, Real-World Examples, and Your First Working Models-logo

Machine Learning Basics: A Practical, No-Math-PhD Guide to Core Concepts, Real-World Examples, and Your First Working Models

Michael Carlsen

Master artificial intelligence and data science fundamentals without drowning in complex math or academic jargon. Perfect for your daily commute, this empowering guide transforms intimidating tech concepts into accessible, career-boosting skills....

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Opening Credits

1/10/2026
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Chapter 1: From Rules To Learning Machines

1/10/2026
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Chapter 2: Data In, Decisions Out

1/10/2026
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Chapter 3: How Machines Learn From Examples And Patterns

1/10/2026
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Chapter 4: From Real Life Problems To Well Framed ML Tasks

1/10/2026
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Chapter 5: Features The Language Models Understand

1/10/2026
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Chapter 6: Fitting, Overfitting, And Learning To Generalize

1/10/2026
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Chapter 7: Judging Models Choosing What Good Enough Means

1/10/2026
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Chapter 8: Meet The Usual Suspects Common Model Families

1/10/2026
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Chapter 9: Your First End To End Models Classification And Regression

1/10/2026
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Chapter 10: Beyond The Basics Neural Nets Ethics And Your Next Steps

1/10/2026
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Closing Credits

1/10/2026