ChatGPT is a game changer. Give your kids the competitive edge by harnessing this transformational technology!
Jumpstart their journey in AI and watch them create apps powered by large language models in Python in this hands-on course.
Now enrolling, limited seats available.
@multiple students Grades: 8-12
Picasso didn't learn by watching lectures. Programming skills are no different.
Active programming is the best way to learn computer science. In this course, students learn concepts by working on curated projects, and get active support from our expert teachers. These are some sample applications that students built within a few weeks using Python and GPT.
Students will learn skills to build complex standalone projects or enhance existing projects with powerful AI-capabilities.
We couldn't have said it better.
It was a very informative class, and it brought someone with a very basic understanding of Python to be able to code their own chatbot. It taught me a lot about the role generative AI.
— Anika G.
At the beginning, I didn't really know if I'd like it or not, but sir's passion for us and his method was really captivating, which ultimately made me enjoy the course.
— Ayan G.
I like how relevant the course is and how our teacher is preparing us for the future. I like how interesting our teacher makes the course. I like how different this course is.
— Aditya G.
I liked the Jupyter Notebook formatted lessons. They were very descriptive, and introduced me to a new platform for me to code on, and actually apply it to websites like Repl.it.
— Yashika A.
I liked how warm and welcoming the class was. When I needed help, others were always ready to provide the knowledge and support for me to succeed.
— Damodar K.
I really appreciated the in-depth google colabs. The homework assignments got me to practice the things I learned from lecture, but they weren't so difficult that I couldn't solve them.
— Rana B.
I liked the aspect of it where we could help answer others questions and how we could play with the Generative AI alongside the teacher.
— Parth A.
I feel the class was very interesting, fun, and I learned a lot. The homework was a little demanding personally because I have a lot of other stuff going on but thats just me.
— Ariv B.
Credits awarded on transcript
Python completed with B- or better
UC A-G approval pending
90 minutes per class
4-8 students per class
Twice per week over 36 weeks
1549 per student, per semester
Self paced instructor-guided
Office hours on-demand
1549 per student, per semester
2-3 hours per day (summer/winter)
4-8 students per class
3 days per week 2, 4, or 6 weeks
589 per student, per week
This is a comprehensive course designed to teach high school students how to build real-world applications in Python using Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), and other Generative AI models. Students will learn Prompt Engineering, Model Fine-tuning, and will build several applications during the course. The skills students learn in this course will enable them to build complex projects for their high school science fairs, hackathons, and other competitions.
Students must have taken an introductory course in Python programming, such as 2Sigma School's Introduction to Computer Science, before enrolling in this course. Prior experience with AI is not required.
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) and ChatGPT is just the tip of the iceberg. To leverage the full potential of LLMs, students need to understand how these models work and how to get the best out of them.
Large Language Models are not magic, and they are not deterministic. Unlike traditional computer programs, how to use these models is not obvious, even to the designers of these models. Prompting techniques are ways to guide the model to produce the desired output. It is a cutting-edge field of study and is still evolving.
In this course, students will start learning to program on Jupyter Notebooks, an essential tool for data scientists and machine learning engineers. They will receive access to the latest API from Open AI and other leading generative AI platforms. They will learn how large language models work, how to use them, and how to get the best out of them. They will learn numerous prompting techniques used by professionals to guide language model to produce the desired output, and will integrate the output of these models in their Python code to build their own generative AI applications such as interactive Chatbots with unique personalities.
After building a few Generative AI applications using Prompt Engineering, students will learn about concepts such as tokenization, embeddings, K-Means clustering, t-SNE visualization, and techniques to fine tune their own large language models from Hugging Face.
By the end of the course, students will be fluent with the prompt engineering, fine-tuning, and building applications for custom use cases using the latest in large language models and generative AI.
This is a new course. University of California A-G approval is awaited.
Our technology requirements are similar to that of most Online classes.
|A desktop or laptop computer running Windows (PC), Mac OS (Mac), or Chrome OS (Chromebook).|
|Students must be able to run a Zoom Client.|
|A working microphone, speaker, webcam, and an external mouse.|
|A high-speed internet connection with at least 10mbps download speed (check your Internet speed).|
Students must have a quiet place to study and participate in the class for the duration of the class. Some students may prefer a headset to isolate any background noise and help them focus in class.
Most course lectures and content may be viewed on mobile devices but programming assignments and certain quizzes require a desktop or laptop computer.
Students are required to have their camera on at all times during the class, unless they have an explicit exception approved by their parent or legal guardian.
This course includes several timed tests where you will be asked to complete a given number of questions within a 1-3 hour time limit. These tests are designed to keep you competitively prepared but you can take them as often as you like. We do not proctor these exams, neither do we require that you install special lockdown browser.
In today's environment, when students have access to multiple devices, most attempts to avoid cheating in online exams are symbolic. Our exams are meant to encourage you to learn and push yourself using an honor system.
We do assign a grade at the end of the year based on a number of criteria which includes class participation, completion of assignments, and performance in the tests. We do not reveal the exact formula to minimize students' incentive to optimize for a higher grade.
We believe that your grade in the course should reflect how well you have learnt the skills, and a couple of timed-tests, while traditional, aren't the best way to evaluate your learning.