The AWS Machine Learning Workshop by Mike Ghen aims to teach participants how to identify relevant data sets for machine learning, train models using AWS, and evaluate their effectiveness. It covers key concepts like supervised learning, feature selection, and model validation, alongside advanced topics such as deploying models and real-time predictions. Practical examples include addressing medical diagnosis and treatment success through classification and regression problems.

1. AWS Machine Learning
Workshop by Mike Ghen

2. Workshop Objectives
Identify data sets and problems where machine learning technologies can be applied
Identify features and target variables in data sets
Refine a training data set to use when build machine learning models
Train machine learning models using AWS Machine Learning
Evaluate machine learning models using AWS Machine Learning
Compute batch predictions using AWS Machine Learning

3. Solving Business Problems with Amazon
Machine Learning
● Supervised learning: learning from data that has been
labeled with the actual answer
● You have existing examples of actual answers
● Acute inflammations in patients
○ You can not code the rules
○ You can not scale

4. Classification, Regression
Examples of binary classification problems:
● Is this patient sick?
Examples of multiclass classification
problems:
● Given possible treatments, which will
succeed?
Examples of regression classification
problems:
● How many days pass before a
chronically ill patient returns?

5. Creating a Data Source
● The target – The answer that you want to predict
● Variables/features – These are attributes of the example
that can be used to identify patterns to predict the target
https://s3.amazonaws.com/mikeghen/acute-inflammations.csv
J.Czerniak, H.Zarzycki, Application of rough sets in the presumptive diagnosis of urinary system diseases, Artifical Inteligence and Security in
Computing Systems, ACS'2002 9th International Conference Proceedings, Kluwer Academic Publishers,2003, pp. 41-51

6. Training and Validation of a ML model
● Splits the training datasource into two sections
● Trains the ML model on the section that contains
70% of the input data
● Evaluates the model using the remaining 30% of the
input data

7. Presumptive Diagnosis:
Acute Inflammations
https://s3.amazonaws.com/mikeghen/acute-
inflammations-holdout.csv
J.Czerniak, H.Zarzycki, Application of rough sets in the presumptive diagnosis of urinary system diseases, Artifical
Inteligence and Security in Computing Systems, ACS 2002 9th International Conference Proceedings, Kluwer
Academic Publishers,2003, pp. 41-51

8. Advanced Topics
● Bootstrapping and Boosting
● Deploying models
○ Building ML Models using custom code
○ Creating an endpoint for real-time predictions
● The importance of evaluation
○ FOREX experiences