You`ve seen automated recommendations everywhere - on Netflix`s home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you`ll become very valuable to them.
Recommender systems are complex; There`s no recipe to follow on how to make a recommender system; you need to understand the different algorithms and how to choose when to apply each one for a given situation.
We assume you already know how to code.
This training is best suited for IT, data management, and analytics professionals looking to gain expertise in AI and ML including: Software Developers and Architects, Analytics Professionals, Senior IT professionals, Testing and Mainframe Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics and Data Science or AI
- You need to have intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
-Intermediate statistics background. You are familiar with probability.
-Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (preferrably a recurrent neural network or a long short-term memory network).
-You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.
Evaluating Recommender Systems
A Recommender Engine Framework
Neighborhood-Based Collaborative Filtering
Matrix Factorization Methods
Introduction to Deep Learning [Optional]
Deep Learning for Recommender Systems
Scaling it Up