There are a number of ways to create a YouTube algorithm that is personalized and uses data from user demographics and watch history to recommend relevant videos to a viewer. One method is the use of a recurrent neural network (RNN), which uses statistics and context to return a list of hundreds of videos. This method works by using data from the YouTube API, as well as from search history and user demographics. While these methods may not be as precise as a human, they are an excellent start.
But these algorithms must be used responsibly. YouTube should consider the methods of teaching their algorithms and take steps to prevent bias in the review process. YouTube should also be open and transparent with advertisers and consumers about its content guidelines. For example, a channel that promotes LGBTQ+ activism or political views may be demonetised. But the opposite may also be true. If a video is promoted only through ads, its click-through rate may tank.
Another option for learning YouTube machine learning is to search through Google’s educational YouTube channels. YouTube has a great series of videos that focus on solving real-world problems. The videos cover a range of topics, from data science to artificial intelligence. While some of the content is quite technical, the videos are designed to spark the creativity of viewers. They provide a great starting point for learning about machine learning. The YouTube platform is home to over 1 billion people around the world, making it the perfect place for budding creators to get started and build a career.
