Machine Learning - Stanford - Coursera
1.0.0
1.0.0
  • Acknowledgements
  • Introduction
  • Linear Algebra Review
  • Types of Machine Learning
  • Supervised Learning
    • Linear Regression
      • Linear Regression in One Variable
        • Cost Function
        • Gradient Descent
      • Multivariate Linear Regression
        • Cost Function
        • Gradient Descent
        • Feature Scaling
        • Mean Normalization
        • Choosing the Learning Rate α
    • Polynomial Regression
      • Normal Equation
      • Gradient Descent vs. Normal Equation
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Acknowledgements

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Last updated 5 years ago

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The contents of this book are based on the lectures by Andrew Ng (Co-Founder of Coursera; Stanford CS adjunct faculty; former head of Baidu AI Group and Google Brain) in the popular Machine Learning course by Stanford University on Coursera.

I'd like to take this opportunity to thank Andrew Ng for the time and effort put into this course and for making the concepts easy to understand while keeping the content as comprehensive as possible.

Note: This book is a work in progress. The previous eBook version of this GitBook can be found .

here