Course Table of Contents
- Course Repository
- Course Introduction
- Part 1 - Docker, TimescaleDB, and Flask
- Part 2 - Dash
- Part 3 - Machine Learning
- Part 4 - Machine Learning Model in Dash
- Part 5 - Testing and Backups
- Part 6 - Deployment
In Part 4, we take the machine learning functions and models we created in a Jupyter Notebook in Part 3, and paste them into our Dash app so we can interactively play with them (e.g. train different models, use different subsets of technical indicator “features”, on different stocks, etc).
Learning Objectives
By the end of Part 4, you will be able to:
- Take a Jupyter Notebook ML model pipeline and deploy it to a Dash website
Next: Finish the Dash Layout
Course Table of Contents
- Course Repository
- Course Introduction
- Part 1 - Docker, TimescaleDB, and Flask
- Part 2 - Dash
- Part 3 - Machine Learning
- Part 4 - Machine Learning Model in Dash
- Part 5 - Testing and Backups
- Part 6 - Deployment
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