MPP On-Demand: Principles of Machine Learning Course Outline
*** Note: This is an On-Demand Self Study Class ***
You can take this class at any time; there are no set dates. It features hands-on labs so you can practice new skills at your workstation. Other parts of the course include video lectures that you can view on-the-go from your phone or tablet. In all cases, customers must call us directly to register this class at 800-288-8221.
MPP On-Demand Series
This course is part of the Microsoft Professional Program Series, MPP: Data Science Track. It can be taken individually or as part of the 9-course series, plus a capstone project. You will need to purchase a validated certificate to get credit for this class as part of the MPP. Request the certificate at time of registration.
On-Demand Learner Profiles
MPP On-Demand is a self-study training solution that was designed for two types of learners. First, MPP On-Demand is a great fit for experienced IT professionals who don't need traditional 5-day classes to upgrade their existing skills. They can pick and choose topics to make the most effective use of their time. Second, MPP On-Demand is perfect for highly-motivated individuals who are new to a technology and need to space their learning over a period of weeks or months. These learners can take their time and repeat sections as needed until they master the new concepts.
Verified Certificate Included!
If you plan to use your course for job applications, promotions, or school applications, an MPP certificate will help you get recognized. The certificate is verifiable and is included in all MPP purchases. Itís also a great way to give yourself an incentive to complete the course and celebrate your success. For more information regarding Verified Certificates, go to
MPP Verified Certificates
Learn how to build, evaluate, and optimize machine learning models; including classification, regression, clustering, and recommendation.
This Microsoft authorized class is an online elearning course that should take between 18 to 24 hours of effort, depending on your background.
Introduction to Classification
Building Classification Models
Introduction to Regression
Creating Regression Models
Improving Machine Learning Models
Principles of Model Improvement
Techniques for Improving Models
Tree and Ensemble Methods
Introduction to Decision Trees
Support Vector Machines (SVMs)
Clustering and Recommenders
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