- Platform
- edX
- Provider
- Microsoft
- Effort
- 6 to 8 hours per week
- Length
- 6 weeks
- Language
- English
- Credentials
- Paid Certificate Available
- Part of
- Course Link
Overview
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, and Azure Notebooks.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
What You Will Learn
After completing this course, you will be familiar with the following concepts and techniques:
Taught by
Graeme Malcolm, Steve Elston, Cynthia Rudin and Jonathan Sanito
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, and Azure Notebooks.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
What You Will Learn
After completing this course, you will be familiar with the following concepts and techniques:
- Data exploration, preparation and cleaning
- Supervised machine learning techniques
- Unsupervised machine learning techniques
- Model performance improvement
Syllabus
- Introduction to Machine Learning
- Exploring Data
- Data Preparation and Cleaning
- Getting Started with Supervised Learning
- Improving Model Performance
- Machine Learning Algorithms
- Unsupervised Learning
Taught by
Graeme Malcolm, Steve Elston, Cynthia Rudin and Jonathan Sanito