AI4ALL: Natural Language Processing
- No prior programming experience needed. You will learn directly in this class.
This course is created to follow up with the AI4ALL initiatives. The course presents coding materials at a pre-college level and introduces a fundamental pipeline for a neural network model. The course is designed for the first-time learners and the audience who only want to get a taste of a machine learning project but still uncertain whether this is the career path. We will not bored you with the unnecessary component and we will directly take you through a list of topics that are fundamental for industry practitioners and researchers to design their customized neural network model. The course follows the previous sequence where we covered Artificial Neural Network models, Convolutional Neural Network models, and Image-to-Image models. This course focuses on some of the most basical tasks in language problems and develop the basic intuition of Recurrent Neural Networks.
This instructor team is lead by Ivy League graduate students and we have had 3+ years coaching high school students. We have seen all the ups and downs. Moreover, we want to share these roadblocks with you. This course is designed for beginner students at pre-college level who just want to have a quick taste of what AI is about and efficiently build a quick Github package to showcase some technical skills. We have other longer courses for more advanced students. However, we welcome anybody to take this course!
Who this course is for:
- Pre-college level students interested in recurrent neural network models
I was a PhD student in Statistics at Columbia University from September of 2020 to December of 2021. I had a B.A. in Mathematics, and an M.S. in Finance from University of Rochester. I have a wide range of research interests in representation learning: Feature Learning, Deep Learning, Computer Vision (CV), and Natural Language Processing (NLP).
I am currently a Senior Data Scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. Prior, I have held professional positions such as enterprise-level Data Scientist at a EURO STOXX 50 company Bayer, quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street. I supervise a small fund specializing in algorithmic trading (since 2011, performance is here) and real estate investment. I also run my own monetarized YouTube Channel.