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. You will spend 3 days performing data science tasks: training models, evaluating them, analyzing data, etc. After this 3 day period you will be better suited to design architecture guidelines for ML production applications. This course is also open to full-time data scientists, who will learn how to perform those tasks on AWS.

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, review sample 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
  Deliverable: Produce the first version of your trained model

Day Three:

• Learn about putting your model into production
• Evalute your project against other approaches
• Design a reference architecture demonstrating your final solution 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.