Machine Learning techniques for developing industry-scale search engines have long been a prominent part of most domains and their online products. Search relevance algorithms are key components of products across different fields, including e-commerce, streaming services, and social networks. In this tutorial, we plan to give a introduction to such large-scale search ranking systems, specifically focusing on deep learning techniques in this area. The topics we plan to cover are the following: (1) Overview of search ranking systems in practice, including popular techniques such as page rank algorithm and BM25; (2) Introduction to sequential and language models in the context of search ranking; (3) Knowledge distillation approaches for this area. For each of the aforementioned sessions we plan to first give an introductory talk and then go over an hands-on tutorial to really hone in on the concepts. We plan to cover fundamental concepts using demos, case studies, and hands-on examples, including the latest Deep Learning methods that have achieved state-of-the-art results in generating the most relevant search results. Moreover, we plan to show example implementations of these methods in python, leveraging a variety of open-source machine-learning libraries as well as real industrial data or open-source data.