Learning Path Overview
QA’s Practical Machine Learning learning path is an intense deep dive into the world of machine learning. In it you'll learn how to implement different machine learning models, validate their quality, and how to implement them practically. This course is being iterated, and we are looking to flesh out the scope and learning activities within it. As such, if you have any feedback, please don’t hesitate to get in touch and let us know what you think we could do to improve this course.  

Intended Audience
This learning path is aimed at fledging data scientists and analysts who wish to gain more in-depth knowledge of Machine Learning.

Prerequisites

• GCSE Mathematics or above. 
• Must be comfortable with analytical and mathematical thinking.
• Familiar with basic python programming: variables, control flow, scope, data structures and functions. Must be comfortable with algorithmic thinking.
• Familiar with basics of data analysis including databases, descriptive statistics, and typical business use cases. 

Learning Objectives

After completing Practical Machine Learning, you will know how to:  

• Explore and prepare data
• Develop ML models
• Pick ML algorithms for a given task
• Understand techniques and metrics used to determine the quality of ML models

Agenda

This learning path contains videos, quizzes and other resources for five modules, together with the associated course Introduction. It also incorporates quizzes for you to test your knowledge as you work through the Learning Path.

Feedback

We welcome all feedback and suggestions - please contact us at qa.elearningadmin@qa.com to let us know what you think.