Principles of Machine Learning

Explore classification
• Understand the operation of classifiers
• Use logistic regression as a classifier
• Understand the metrics used to evaluate classifiers
• Lab: Classification with logistic regression taught using Azure Machine Learning

Regression in machine learning
• Understand the operation of regression models
• Use linear regression for prediction and forecasting
• Understand the metrics used to evaluate regression models
• Lab: Predicting bike demand with linear regression taught using Azure Machine Learning

How to improve supervised models
• Process for feature selection
• Understand the problems of over-parameterization and the curse of dimensionality
• Use regularization on over-parameterized models
• Methods of dimensionality reduction Apply cross validation to estimating model performance
• Lab: Improving diabetes patient classification using Azure Machine Learning
• Lab: Improving bike demand forecasting using Azure Machine Learning

Details on non-linear modeling
• Understand how and when to use common supervised machine learning models Applying ML models to diabetes patient classification
• Applying ML models to bike demand forecasting Clustering
• Understand the principles of unsupervised learning models
• Correctly apply and evaluate k-means clustering models
• Correctly apply and evaluate hieratical clustering model
• Lab: Cluster models with AML, R and Python

Recommender systems
• Understand the operation of recommenders
• Understand how to evaluate recommenders
• Know how to use alternative to collaborative filtering for recommendations
• Lab: Creating and evaluating recommendations