Hands-On ML, Edition 2 by Aerolien Geron, Summary Part 1

This is the first part of the 2 part series by Aaron Ma on the summary of Hands-On ML.

Pinterest image. Copyright Aerolien Geron and respective authors.

I. The Fundamentals of Machine Learning

This book is divided into 2 sections. This is the 1st section’s summary. You can find the 2nd section’s summary here. The 1st section covers the following:

  • Typical steps in training ML models
  • Training ML models
  • Optimizing a loss function
  • Preparing data
  • Engineering features
  • One-hot encoding
  • Most common ML models
  • Multioutput classification & Multilabel classification
  • Validating models
  • 2. Data analysis (remove useless features, plot graphs, etc.)
  • 3. Select an algorithm
  • 4. Create a model.
  • 5. Evaluate the model’s performance.
  • 6. Fine-tune the models and combine them into a solution.
  • 7. Present your solution.
  • 8. Deploy and monitor your solution.
  • Confusion Matrix
  • Metric Options
  • Precision
  • Recall
  • F1 score
  • AUC/ROC
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