Содержание
Event Information
| Category | Details |
|---|---|
| Workshop Active? | No, too long for virtual events |
| Reactor Topic | Data Science & Machine Learning |
| Location | In person |
| Workshop Level | Beginner |
| Workshop Duration | 6 hours |
| Title | Beginners Data Science for Python Developers |
| Description | Every day new data is created. New parts are made and shipped from factories, people continuously tweet, and companies grow and fluctuate causing major changes in the market. With the addition of more data comes the difficulty of being able to process that data. As humans, we can understand complex scenarios, but computers are much better at being able to analyze large datasets. In this workshop, you will get a glimpse into how we can teach machines to analyze complex scenarios at a much larger scale than we're able to. After you've cleaned and organized your data, you will have an opportunity to train and test machine learning models, and even publish your predictor online for others to explore. You do not need any prior experience with data science to attend this workshop. You are likely someone who has ~1 year experience coding, preferably in Python, but not a requirement. You are interested in learning how to use Python libraries to call machine learning models and make predictions on your data. You should bring your own laptop (Windows or Mac) with an Internet browser. You will be using Azure Notebooks, a cloud-based Jupyter Notebooks instance, and Azure Machine Learning Studio. All you will need is a Microsoft Account, which only requires an email address and for which you can sign up for at the event. |
| Content Link | https://github.com/microsoft/Reactors/tree/main/workshop-resources/data-science-and-machine-learning/Data_Science_1/workshop-materials |
Event Dates Run:
Here's an overview of what this workshop will cover and suggested agenda to follow with links and resources.
Workshop Agenda
30 minutes Introduction to Data Science Keynote
60 minutes Cleaning and Manipulating Data
30 minutes Lunch
1.5 hours Introduction to Machine Learning Models
1.5 hours Machine Learning Studio
30 minutes Wrap up and Next Steps
Resources
- TBD