Duration
6 weeks (3-4 hours per week)
About this course
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.
Length: 6 weeks
Effort: 3-4 hours per week
Level: Intermediate
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
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
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
This is a version-specific reference outline. Confirm the current syllabus and delivery arrangements with EIEIO.