Learn to code, debug, and analyze data with AI assistance in Jupyter notebooks

Welcome to Jupiter AI. Coding by hand,

where you use your 10 fingers to write

every line of code manually, is becoming

obsolete. And coding notebooks,

including those that we use on the deep

learning.ai site, have to evolve away

from that to using AI to code for you.

Together with Brian Granger, who is

co-founder of project Jupiter, I'm

delighted to introduce Jupiter AI. You

better use it on the deep learning.AI AI

site or on your own computer by running

the open-source Jupiter locally. Jupyter

notebooks are a workhorse of AI

development of data science and also

views for teaching AI by deep

learning.ai.

Moving it forward to the AI coding era

will be an important step for our few.

So I'm thrilled to be teaching this with

you Brian.

>> Thank you, Andrew. When we created the

IPython notebook in 2011 and later

rebranded it as the Jupyter notebook in

2014, we did so with the mission of

creating a communitydriven open-source

ecosystem of tools for data science,

scientific research, and education.

Jupyter notebooks have long been the

default prototyping environment for this

type of work. But it turns out that most

of the AI coding assistants that have

emerged in the last couple of years have

struggled to function well in a Jupyter

notebook environment. And so we've built

Jupyter AI which is an open source

framework that is purpose-built to

integrate AI into Jupyter notebooks and

Jupyter Lab. Jupy AI can help you write

code, let you ask questions about codes

and so on. tasks you might already be

familiar with from other AI coding

tools, but it is deeply integrated into

Jupyter notebooks and can also do things

that other coding assistants struggle

with today. For example, you can

generate new cells in your notebook

directly from the chat or drag a cell to

the chat and ask questions about the

code or drag a cell with markdown

instructions and use that to generate

code. You can also use it to debug

errors in your notebook or attach

additional context for the alm. Jupyter

AI is an AI coding assistant is also a

brainstorming partner, a collaboration

platform, and a great tool for learning.

>> That's right. And so in this course,

you'll see how easy it is to build out

AI and data science workflows from

scratch using Jupyter AI.

>> It is important that notebooks make the

transition from coding by hand to coding

with AI. If you use code notebooks and

want to learn to navigate this

transition or if you want to learn how

to use coding notebooks but in a modern

way, please take this course.