Welcome to Python 200! In this class, you will build on your foundation from Python 100, with a focus on supporting and maintaining modern data pipelines. Data pipelines today increasingly involve machine learning models, AI components, and cloud infrastructure -- this course prepares you to work with all three.
Over 11 weeks, you will get hands-on experience with the tools that show up in these pipelines -- scikit-learn, LlamaIndex, Prefect, Azure, and more. Each week you will come away with projects that demonstrate what you can build.
The course is organized into four modules:
- Module 1: Analysis (Week 1): Review of Python 100, and introduction to data analysis and pipelines.
- Module 2: Machine Learning (Weeks 2-4): Overview of machine learning, with hands-on regression, classification, and deep learning lessons.
- Module 3: AI (Weeks 5-7): Introduction to large language models. Hands-on lessons on chatbots, augmentation of LLMs with new knowledge, and AI-based agents.
- Module 4: Cloud computing (Weeks 8-11): An overview of cloud computing with Microsoft Azure. These include building an AI application, ML pipelines, and ETL pipelines in the cloud.
To get started, head over to the lessons landing page for more details on setting up your environment and getting started with your lessons.
This course is maintained by a community of contributors. Each contributor brings their own experience and teaching style. As a result, you will likely see differences in pacing, terminology, and emphasis between lessons. You will also see some repetition.
We view this as a strength. Real technical teams are made up of people with different backgrounds and perspectives. Learning from multiple voices will help develop a more rounded understanding of the material.
For details on how to contribute to the course material, please see CONTRIBUTING.md. If you have suggestions, or find issues or errors, please open an issue, which is one great way to contribute!