Your mouse clicks on the internet leave a trail behind, a trail that can be a valuable source of information for companies. Businesses all over the world can capture data to create evermore personalised user experiences, passing on recommendations, and ultimately increasing user engagement and sales. Take this easy to manage course and you’ll learn about the core tasks recommendation systems perform, and you’ll even be able to build your own themed recommendation system.
What Does This Course Focus On?
This course is around 4.5 hours worth of intensive training where you’ll get to grips with recommendation systems, the theory, and how they are built and used. This course includes both theory and practice, so by the end you can have a go at building your own recommendation system. Your e-learning journey will include a look at Movielens, Pandas, and you’ll get to grips with Scipy and NumPY software, systems under the Python umbrella.
Why Invest In This Course?
This course has no real pre-requisites, although knowledge of undergraduate level maths and/or the Python computing language would be beneficial. With this in mind, the training provided has broad benefits for a range of careers, whether you are working as an analytics professional, tech executive, or even a product manager that wants greater understanding to enable effective communication with data specialists.
KEY LEARNING POINTS
Gain an understanding of this valuable machine learning technique and its practical application. Complete the course and you’ll be able to build your own recommendation system in Python.
Learn more about the well-known MovieLens website and search functions, and how you can build a profile and get recommendations based on your taste, ratings, and commonly used tags.
Get to grips with Pandas, and learn more about the software library and its role in data manipulation and analysis.
Become familiar with Scipy, the popular open source software commonly used in maths, science and engineering. It’s Python based so you’ll learn more about the systems capabilities.
You’ll also become familiar with NumPY another key Python based package, and one which is known for integrating seamlessly with a variety of different databases.
Work through an introduction to both latent factor methods and memory based approaches within this machine learning discipline.
By the end of the course you will be able to design, and then implement, a recommendation system in Python based on movies.
This is a course that covers some fundamental concepts including matrix factorisation and neighbourhood models.
ADVANTAGES OF THIS COURSE
No previous qualifications required for this course, although under grad level maths and knowledge of Python is useful.
Course is suitable for a wide range of professionals from programmers to product managers.
Learn from material prepared by experts with decades of experience.
An affordable study option to fit around daily life.
A comprehensive look at a popular and relevant machine learning concept.
Gain a thorough understanding of how online recommendations work, and the software used to capture data.