Machine Learning Bookcamp


These articles are based on the core concepts from the book


Prerequisites (optional

Linear regression

  • Linear Regression (coming soon)
  • Training linear regression: Normal Equation (coming soon)
  • RMSE: Root mean squared error (coming soon)

Feature importance

  • Risk Ratio (coming soon)
  • Mutual Information (coming soon)

Feature engineering

  • One-Hot Encoding (coming soon)

Logistic regression

  • Logistic regression: training (coming soon)
  • Logistic regression: interpretation (coming soon)

Evaluating classification models

  • Accuracy (coming soon)
  • Confusion table (coming soon)
  • Precision and Recall (coming soon)
  • ROC Curve (coming soon)
  • Area under the ROC curve (coming soon)
  • Cross-Validation (coming soon)

Deploying machine learning models

  • Saving and loading models with Pickle (coming soon)
  • Introduction to Flask (coming soon)
  • Serving models with Flask (coming soon)
  • Managing dependencies with Pipenv (coming soon)
  • Containerization with Docker (coming soon)
  • Deploying models with AWS Elastic Beanstalk (coming soon)

Kubernetes and Kubeflow


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