Fundamentals of Machine Learning
What you'll learn
- Learn about the fundamental principles of machine learning
- Build customized models to use for different data science projects
- Build customized Deep Learning models to start your own data science career
- Start your data science career and connect with the tutor in industry
- No prior mathematical or programming knowledge required. Some python programming experience is helpful.
This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.
The course is comparable to the Introduction of Statistical Learning, which is the intro course to machine learning written by none other than the greatest of all: Trevor Hastie and Rob Tibshirani! The course was modeled from the "Introduction to Statistical Learning" from Stanford University.
The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry.
The course has the following topics:
Basics in Statistical Learning
Sampling and Bootstrap
Model Selection & Regularization
Going Beyond Linearity
Support Vector Machine
The course is composed of 3 sections:
Lecture series <= Each chapter has its designated lecture(s). The lecture walks through the technical component of a model to prepare students with the mathematical background.
Lab sessions <= Each lab session covers one single topic. The lab session is complementary to a chapter as well as a lecture video.
Python notebooks <= This course provides students with downloadable python notebooks to ensure the students are equipped with the technical knowledge and can deploy projects on their own.
Who this course is for:
- Beginners in python programming, machine learning, and data science.
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.