Welcome to the art and science of machine learning! During this 3-day course you will learn about the theory and application of machine learning in industry. This course is designed for architects and developers who did not previously have a background in AI/ML, providing intuition and confidence in designing ML applications.
We will cover:
- Statistical machine learning
- Deep Learning
- Feature engineering
- Deploying a model into production
- Model evaluation and comparison
As a prerequisite to attending this course, we recommend reviewing Python programming using the statistical package Pandas. We also recommend having a Cloud Practiioner AWS Certification, but it is not required. Lastly, we recommend the book listed below. It is an excellent read, and clearly demonstrates all important concepts.
- https://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/
- https://aws.amazon.com/certification/certified-cloud-practitioner/
- Deep Learning with Python by Francois Chollet
Day One:
- Learn about ML on AWS
- Go through a sample lab
- Break into teams and focus on a new machine learning project
Deliverable: Produce a sample writeup explaining your modeling strategy
Day Two:
- Learn about feature engineering on AWS
- Start new notebooks, sample your code, and develop preliminary data sets
- Read the evaluation questions, and begin to think about how your modeling strategy compares to the evaluation questions.
- Finish most of your feature engineering.
Stretch goal: produce a reference architecture explaining how you would like to use this model in production
Day Three:
- Learn about putting your model into production.
- Format your data into X's and Y's
- Produce a preliminary version of your model
Stretch goal: produce multiple versions of your model and compare them
- AWS Account log in credentials
- Github account to share code with your project partners
- Kaggle account to download data sets
We're assuming that you will complete this course using an AWS account we will provide you with throughout the course. If you would like to use your own, you are welcome to, but we cannot guarantee technical success in other accounts.
You are welcome to share your code publicly with your teammates, in which case you can use the code elsewhere.