Each participant should be familiar with Python. Basic Linux command line skills are valuable but not required. Each participant will be required to run a 64 bit virtual machine (provided with the course).
Machine learning is a type of artificial intelligence wherein computer programs learn new capabilities when exposed to data. This course teaches the basics of machine learning with practical hands on labs using Python and various support libraries. Day one introduces the foundational concepts of data science and machine learning. Hands on labs progressively build a basic collection of tools and experiments reinforcing the concepts covered in lecture.
Participants will learn how to create a basic Python development environment for machine learning while producing several basic but useful and instructive programs. Basic probability, statistics and basic data curation skills are developed throughout. Days two and three build on the foundational skills imparted in day one, introducing formal classification of the most common machine learning algorithms and their purposes. Modules and labs give participants experience using the most common algorithms and a chance to create real solutions, such as fraud detection and recommendation engines.
This course is designed for Application developers, analysts and data scientists.
Upon completion of this course, participants will be able to:
- Have a broad but practical understanding of machine learning and a base from which to pursue real applications and further study
Data Science and Machine Learning
Probability and Statistics
Working with and curating data
The machine learning process
Linear and polynomial regression
Decision trees and ensemble methods
Probability based learning
Evaluation and hyperparameter tuning