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edX Principles of Machine Learning

Microsoft via edX

  • Overview
  1. edX
    Platform:
    edX
    Provider:
    Microsoft
    Length:
    6 weeks
    Effort:
    3-4 hours a week
    Language:
    English
    Credentials:
    Paid Certificate Available
    Part of:
    Microsoft Professional Program in Artificial Intelligence
    Overview
    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.

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

    Taught by
    Dr. Steve Elston and Cynthia Rudin

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