Articles

These articles are based on the core concepts from the book

Introduction

• Supervised Machine Learning (coming soon)
• CRISP-DM: The methodology for machine learning projects
• Model selection (coming soon)

Prerequisites (optional

• Creating an AWS account
• Renting an EC2 instance
• Introduction to Python
• Introduction to NumPy
• Linear algebra refresher (coming soon)
• Introduction to Pandas (coming soon)

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

• Creating an EKS Cluster
• Creating a KFServing Cluster on EKS
• KFServing Transformers

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