  Job transitions and upskilling are common actions taken by many industry
working professionals throughout their career. With the current rapidly
changing job landscape where requirements are constantly changing and industry
sectors are emerging, it is especially difficult to plan and navigate a
predetermined career path. In this work, we implemented a system to automate
the collection and classification of training videos to help job seekers
identify and acquire the skills necessary to transition to the next step in
their career. We extracted educational videos and built a machine learning
classifier to predict video relevancy. This system allows us to discover
relevant videos at a large scale for job title-skill pairs. Our experiments
show significant improvements in the model performance by incorporating
embedding vectors associated with the video attributes. Additionally, we
evaluated the optimal probability threshold to extract as many videos as
possible with minimal false positive rate.
