Learning Path Overview
QA’s Practical Machine Learning course 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.
This course is aimed at fledging data scientists and analysts who wish to gain more in-depth knowledge of Machine Learning.
- 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.
After completing the Practical Machine Learning course, you will know:
- How to explore and prepare data
- Develop ML models
- How to pick ML algorithms for a given task
- Understand techniques and metrics used to determine the quality of ML models
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.
We welcome all feedback and suggestions - please contact us at email@example.com to let us know what you think.
Learning Path Steps
Machine learning is a big topic. Before you can start to use it, you need to understand what it is, and what it is and isn’t capable of. In this module we’ll start with the basics, introducing you to AI and its history. We’ll discuss the ethics of it, and t...
One of the tools you can use to programme machine learning. In this module, you’ll learn the basics of python when it’s used for machine learning, how to use loops to compute total loss, regressions and classification, and how to setup machine learning in p...
To design effective machine learning, you’ll need a firm grasp on the maths that support it. In this module, we’ll introduce you to the mathematics of machine learning, before jumping into common functions and useful algebra, the quadratic model, and logari...
Supervised learning is a core part of machine learning, and something you’ll probably use quite a lot. In this module, you’ll learn more about exactly what it is and what its capable of, before we move into more detail about the nearest neighbours algorithm...
Using SageMaker Notebooks to Train and Deploy Machine Learning Models
In this lab, you'll use a SageMaker notebook to learn how to write Python code to prepare data, train and deploy models, and use them for real-time inference.
Selecting the right machine learning model will help you find success in your projects. In this module, we’ll discuss how to do so, as well the difference between explanatory and associative approaches, before we end on how to use out-sample performance.
Predict income levels using census data and compare the performance of two trained models in this Azure Machine Learning Studio Lab.
Regression is a widely used machine learning and statistical tool and it’s important you know how to use it. In this module, we’ll discuss interpreting modes, as well as how to interpret linear classification models.
Take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform CPU vs GPU performance analysis for AWS Machine Learning in this Lab.
About the Author
QA is the UK's biggest training provider of virtual and online classes in technology, project management and leadership.