Catie Edwards is a Machine Learning Engineer at Capital One. In addition to that she is teaching Machine Learning at the WIT Experience Tech College track. Capital One’s Tech College is a first-of-its-kind, engineer-led learning hub that gives its associates the tools and the platform to gain and master deeper technical skills.
Tell us about yourself
I am from Chicago and went to the University of Michigan, studying Computer Science and Engineering — after college, I moved to D.C. to work at Capital One.
Art has always been a hobby — I try to take sculpture or painting classes when I can. I sail, too!
You started out as a Data Network Engineer after college. How did you transition to a Machine Learning Engineer role?
My journey into Machine Learning spring boarded from my experience in the Cyber team at Capital One. My previous professional background was in Cyber but after partnering with the Big Data team on a project, I realized I missed building those types of solutions. I made the jump from working as a Cyber engineer to my current team, and am working on Machine Learning.
I’ve been in the Machine Learning organization for over a year. It’s been an awesome journey — our team is growing and we are doing a lot of interesting projects.
What are some resources (books, blogs, courses, etc.) that you recommend for Machine Learning?
While I was working in Cyber, I took some classes on Coursera about Machine Learning, and that helped drive my interest.
Capital One’s Tech College has some great information and resources for associates.
What will you be teaching at Women in Tech Experience on the topic of Machine Learning? What will the audience learn?
I will be teaching an Introduction to Image Classification using Machine Learning. Its’s like the ‘Hello World’ of Machine Learning. [For most people who start in Computer Science, figuring out how to program ‘Hello World’ is a basic, first project.]
We are going to teach students to classify images using Machine Learning. We will go through a few different algorithms; how to set up the initial environment; how to download the data set; how to use libraries; and the ins and outs of training and testing your first model.
What has your experience been like teaching at Tech College?
This is my first time teaching this class. I’ve taught other lecture style classes before, but my co-worker, Minh “Jennifer” Van, actually developed this course. The class itself is a good level — it assumes you have basic programming skills, but you don’t need any Machine Learning experience beforehand.
The class is designed to take some of the intimidation out of Machine Learning and make it feel more accessible to people.
I am excited to teach this course to an external audience, and women especially. We have so many resources internally, so it’s great to be able to share our resources with women who might not have similar access to knowledge and training. I love being able to share this information, and hopefully inspire women to try their hand at Machine Learning.
What advice would you give to someone who is considering a career in tech?
Be confident. When I graduated and started working, it felt overwhelming sometimes, and took me a few years out in my career to recognize and appreciate my skills and knowledge. But I am just as good as anyone else, and always have been. I wish I realized that sooner.
For me, I didn’t know much about Machine Learning after college, but I took some initiative, took a course to learn more, and that paved the way for the opportunity with my current team. When I interviewed to switch roles, I knew the basics of Machine Learning and knew I loved it — and they were OK that I didn’t know everything. It’s better to know what you know, and know what you don’t know — but realize you will learn as you go.
My other advice is to try something, see if you like it — and even if you aren’t 100% confident in your skills, know that baseline knowledge is usually all you need.
I’ve taken my time to figure out what I want to do. Through college, to realizing I was good at programming, through my experience in Cyber, to my current role in Machine Learning — it’s been a journey. I think people can be intimated by others who seem so far ahead in their careers. But I reject the idea that it’s ever too late to switch careers. You build off your previous experiences and you’re better for it. Never be intimated to switch your career path.
Check out Capital One’s WIT Experience and join women in tech from all over for a day of skill-building, networking and fun. Learn about Machine Learning and many other topics.
Thank you Capital One for sponsoring this post! To learn more about Capital One, visit www.capitalonecareers.com.