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Machine Learning Crash Course with TensorFlow APIs

Google via Independent


A self-study guide for aspiring machine learning practitioners. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

Some of the questions answered in this course:

  • Learn best practices from Google experts on key machine learning concepts.
  • How does machine learning differ from traditional programming?
  • What is loss, and how do I measure it?
  • How does gradient descent work?
  • How do I determine whether my model is effective?
  • How do I represent my data so that a program can learn from it?
  • How do I build a deep neural network?


ML Concepts

  • Introduction
  • Framing
  • Descending into ML
  • Reducing Loss
  • First Steps with TF
  • Generalization
  • Training and Test Sets
  • Validation
  • Representation
  • Feature Crosses
  • Regularization: Simplicity
  • Logistic Regression
  • Classification
  • Regularization: Sparsity
  • Introduction to Neural Nets
  • Training Neural Nets
  • Multi-Class Neural Nets
  • Embeddings

ML Engineering

  • Production ML Systems
  • Static vs Dynamic Training
  • Static vs Dynamic Inference
  • Data Dependencies

ML Real World Examples

  • Cancer Prediction
  • 18th Century Literature
  • Real-World Guidelines


  • Next Steps


4.0 rating, based on 1 Class Central review

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  • Henderik Bond
    For a 15 hours course (it took me far longer than that), it does pretty well on presenting the basic theory necessary to apply it in easy but still increasingly complex exercises. It works on their own servers, so no installation is required, and it is based on Python while using the TensorFlow library (which makes sense, being both from Google).

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