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Principles of Machine Learning Course Outline

 (3 days)

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

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.

Approximately 6 weeks assuming 3 to 4 hours of effort per week.

What you'll learn
Explore classification
Regression in machine learning
How to improve supervised models
Details on non-linear modeling
Recommender systems
The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

Course Syllabus

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

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
View outline in Word


Attend hands-on, instructor-led Principles of Machine Learning training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

For additional training options, check out our list of Courses and select the one that's right for you.

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If you have high-speed internet and two computers you can likely take this class from your office or home.

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