How to use Jupyter Notebooks to do Machine Learning and AI Tasks

This video walks through the use of Jupyter Notebooks for performing Machine Learning and AI tasks. It demonstrates how to load a Jupyter Notebook from GitHub into Google Colab, provides a primer on Notebooks and how they work and what Google Colab is, and covers several important topics for those new to Notebooks or wanting to understand them better, including:
How to load and use secrets like API keys in Jupyter Notebooks securely
How to load a Notebook from GitHub into Google colab
How cells work in Jupyter Notebooks
Ideal use cases for Notebooks, sharing with your team
How you can accidentally leak secrets via Notebooks
How to fix common problems that prevent loading a Notebook in Google colab
How semantic search, backed by a vector database, works
How Scope works in Jupyter Notebooks and code cells
Sharing Notebooks with teammates and considerations around security
Timestamps
00:25 Intro
00:51 What are Jupyter Notebooks?
1:24 Finding the Getting Started guide
2:04 The Jupyter Notebook file format. Integration with GitHub
2:52 What are cells?
3:48 Why you need to understand the security implications of using Notebooks
4:16 Why are Notebooks so popular?
4:40 My experience with Notebooks as an application/infrastructure developer
5:27 The semantic similarity search example Notebook we’ll be using
6:10 What Notebooks are ideal for - which use cases
7:00 How the Google Colab badge/button works
7:30 Why do we need Google Colab at all?
8:30 The initial Gotchas preventing smooth loading of a Notebook in Colab
10:45 How code cells work
11:46 What do ! exclamation points mean in front of commands in cells?
12:42 How scope works in Jupyter Notebooks
13:33 Different running modes for Jupyter Notebooks
14:25 How you can use Notebooks to help you test things
15:25 How to securely work with secrets like API keys
16:00 What are secrets and why are they important?
18:20 Loading your Pinecone API key securely
20:49 Working with Pinecone Indexes
21:36 The original Kaggle challenge dataset we’re using in this Notebook
22:14 How the download data function works
22:52 Upserting vectors to Pinecone’s vector database
23:45 How to query the Pinecone database via semantic search
24:42 Evaluating the results we get back