The course teaches students how to start looking at data with the lens of a data scientist by applying efficient, well-known mining models in order to unearth useful intelligence, using Python, one of the popular languages for Data Scientists. Topics include data visualization, feature importance and selection, dimensionality reduction, clustering, classification and more! All of the data sets used in this course are gathered live-data or inspired by real-world domains that can benefit from machine learning.
What you’ll learn
- What machine learning is and the types of problems it is adept to solving
- How to represent raw data in a manner conducive to deriving valuable information
- How to use various data visualization techniques
- How to use principal component analysis and isomap intelligently to simplify your data
- How to apply supervised learning algorithms to your data, such as random forest and support vector classifier
- Concepts such as model selection, pipelining, and cross validation