Skip to main content

Introduction to Zoe's Notes

Let's go through key concepts in Data Science - Machine Learning.

Overview​

This is a work in process in attempt of organizing knowledge that I learned and read.

What You'll Learn​

Throughout this documentation, you'll gain insights into:

  • Exploratory Data Analysis
  • Data Preparation
  • Feature Engineering and Selection
  • Model Training
  • Model Evaluation and Visualization
  • Testing
  • Architectural Components
  • Packaging and Containerization
  • Continuous Integration and Continuous Deployment (CI/CD)
  • Monitoring and Visualization
  • Handling Data Drift
  • Implementing Feedback Loops
  • Serving Models

Getting Started​

To get the most out of this guide, you should have:

  • Basic understanding of Python programming
  • Familiarity with machine learning concepts
  • Interest in deploying and maintaining machine learning models in production environments

Let's begin our journey through the End-to-End Machine Learning Cycle!