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

Pinterest image. Copyright Aerolien Geron and respective authors.

I. The Fundamentals of Machine Learning

  • What ML is, the difference between Traditional Learning and ML, etc.
  • 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
  • 1. Get the data
  • 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.
  • Use Cross-Validation (K-Folds)
  • Confusion Matrix
  • Metric Options
  • Precision
  • Recall
  • F1 score
  • AUC/ROC
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  • Clustering
  • Anomaly detection
  • Density estimation

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