README.md

Notes

Here are the notes I took from the course's videos.

• Session 1: Introduction to Machine Learning
• Session 2: Linear Regression
• Session 3: Binary Classification (Logistic Regression)
• Session 4: Evaluation Metrics for Classification Models
• Session 5: Deploying Machine Learning Models
  • Extra: virtual environments in Intel Macs
• Session 6: Decision Trees
• Session 7: Midterm project
  • Extra: Office hours notes
• Session 8: Deep Learning
• Session 9: Serverless Deep Learning
• Session 10: Kubernetes and TensorFlow-Serving
• Session 12: Capstone project

Additionally, the following gists with cheatsheets are available.

• Virtualization and containerization
• Python and data libraries cheatsheet
• Python virtual environment management
• Git cheatsheet