How to Use Jupyter Notebooks for Machine Learning and AI Tasks

Explore the world of Jupyter Notebooks for Machine Learning and AI tasks in this comprehensive 34-minute video tutorial. Learn how to load Notebooks from GitHub into Google Colab, understand the basics of Notebooks and their functionality, and discover Google Colab's features. Dive into crucial topics such as securely handling API keys, cell operations, ideal use cases, potential security risks, and troubleshooting common loading issues. Gain insights into semantic search with vector databases, scope management in code cells, and best practices for sharing Notebooks with team members. Follow along as the tutorial demonstrates practical examples, including working with Pinecone Indexes, handling datasets, and performing semantic searches using a vector database.

Intro
What are Jupyter Notebooks?
Finding the Getting Started guide
The Jupyter Notebook file format. Integration with GitHub
What are cells?
Why you need to understand the security implications of using Notebooks
Why are Notebooks so popular?
My experience with Notebooks as an application/infrastructure developer
The semantic similarity search example Notebook we’ll be using
What Notebooks are ideal for - which use cases
How the Google Colab badge/button works
Why do we need Google Colab at all?
The initial Gotchas preventing smooth loading of a Notebook in Colab
How code cells work
What do ! exclamation points mean in front of commands in cells?
How scope works in Jupyter Notebooks
Different running modes for Jupyter Notebooks
How you can use Notebooks to help you test things
How to securely work with secrets like API keys
What are secrets and why are they important?
Loading your Pinecone API key securely
Working with Pinecone Indexes
The original Kaggle challenge dataset we’re using in this Notebook
How the download data function works
Upserting vectors to Pinecone’s vector database
How to query the Pinecone database via semantic search
Evaluating the results we get back