Principles of Machine Learning

This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, Python, and Azure 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