Learn to use Jupyter AI, which adds generative AI capabilities to Jupyter Lab and Jupyter Notebook, the most popular open-source tools for Python programmers, Data Scientists, and AI & Machine Learning Engineers.
AI Coding with Jupyter AI
46m 33s
English
Paid
AI Coding with Jupyter AI is a 8-lesson 46 minutes self-paced course by Zero To Mastery. Learn to use Jupyter AI, which adds generative AI capabilities to Jupyter Lab and Jupyter Notebook, the most popular open-source tools for Python programmers, Data Scientists, and AI & Machine Learning Engineers.
Course facts
- Lessons
- 8
- Duration
- 46 minutes
- Level
- All levels
- Language
- English
- Updated
- Instructor
- Zero To Mastery
- Price
- Premium
With Jupyter AI you can easily add powerful machine learning models and sophisticated AI tools to your Jupyter Lab and Jupyter Notebook, all without the need for extensive coding knowledge. If you're out to become a top 10% Python programmer, you'll want to learn Jupyter AI.
Who teaches AI Coding with Jupyter AI? Zero To Mastery
Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.
The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.
The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.
What lessons are included in AI Coding with Jupyter AI?
0:00
/ #1: Introduction
All Course Lessons (8)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 05:44 | |
| 2 | Introduction to Jupyter AI | 03:40 | |
| 3 | Installing Jupyter AI | 04:16 | |
| 4 | Using Jupyter AI in Jupyter Labs | 12:21 | |
| 5 | Setting Up Jupyter AI in Jupyter Notebook | 04:39 | |
| 6 | Using Jupyter AI in Jupyter Notebook | 06:18 | |
| 7 | Using Interpolation for Advanced Use Cases | 05:25 | |
| 8 | Using Jupyter AI with Other Providers and Models | 04:10 |
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Frequently asked questions
What prior knowledge should I have before taking this course?
Before enrolling in this course, students should have a basic understanding of Python programming. Familiarity with Jupyter Lab and Jupyter Notebook is also recommended, as the course builds on these tools to introduce Jupyter AI and its features.
What will I learn to build during the course?
Students will learn to integrate and use Jupyter AI within Jupyter Lab and Jupyter Notebook, enabling the addition of generative AI capabilities to their existing projects. The course covers setting up Jupyter AI and applying it to advanced use cases using interpolation and other models.
Who is the intended audience for this course?
The course is designed for Python programmers, Data Scientists, and AI & Machine Learning Engineers who are interested in extending the capabilities of Jupyter Lab and Notebook with generative AI features. It is suitable for professionals looking to enhance their data science and AI toolsets.
How does the depth of this course compare to other AI courses?
This course specifically focuses on the integration of Jupyter AI into Jupyter Lab and Notebook, highlighting its generative AI capabilities. Unlike broad AI courses, it provides a specialized approach to leveraging Jupyter AI with practical applications like interpolation and model integration.
What specific tools or platforms are covered in this course?
The course covers Jupyter AI, an extension for Jupyter Lab and Jupyter Notebook. It details installation, setup, and usage in both environments, including advanced use cases with interpolation and integration with other AI providers and models.
What topics are not covered in this course?
The course does not cover foundational AI concepts or broader data science techniques unrelated to Jupyter AI. It focuses specifically on the practical application of Jupyter AI within the Jupyter environment, rather than general AI theory or Python programming fundamentals.
How does taking this course benefit my career in AI or data science?
By completing this course, students will enhance their ability to use generative AI within Jupyter Lab and Notebook, tools widely used in the data science and AI fields. This skill set can improve project workflows and expand possibilities for AI model experimentation and data analysis, making it a valuable addition to a professional's toolkit.