Get hands-on experience building and deriving insights from machine learning models using R and Azure Notebooks.
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.
To complete this course successfully, you should have:
- A basic knowledge of math
- Some programming experience – R is preferred.
- A willingness to learn through self-paced study.
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
- Introduction to Machine Learning
- Exploring Data
- Data Preparation and Cleaning
- Getting Started with Supervised Learning
- Improving Model Performance
- Machine Learning Algorithms
- Unsupervised Learning