Andrew Maas is currently at Apple working on data-centric deep learning. He completed a PhD in Computer Science at Stanford in 2015 advised by Andrew Ng and Dan Jurafsky. His dissertation focused on large scale deep learning methods for spoken and written language. Andrew has worked as an engineer and scientific advisor to several startups including Wit.ai, Coursera, and Semantic Machines. Prior to Apple, he built an NLP platform for precise healthcare language as cofounder of Roam Analytics. Additionally he also teaches CS224S: Spoken Language Processing as a visiting lecturer at Stanford University.
Co:rise is young but already in a league of its own when compared to other online upskill/career changing courses. Courses are intimate and students are driven. It felt more like an accelerated university level course than on online certificate program in that I learned more in 4 weeks that I have with any other online course. The most unique part is that you get facetime with instructors and mentors who have proven track records in the domain they are teaching.
This course gave me the boost I needed in my day job to keep up with ML topics and my ML engineer colleagues. The pace and time needed for the course fit nicely into my busy schedule while also giving me tangible skills quickly. Real-time support from the Uplimit team was a key part of my success. I highly recommend this course!
Taking the Applied Machine Learning course has been an incredible experience. We not only learned tactical skills to approach building state-of-the-art ML models, but also learned important ideas on how to properly setup ML teams, formulate problems, and think about ethics. All of this was supplemented with fireside chats with industry ML practitioners and leaders who talked about their experiences building teams and integrating ML into their products. It's been an amazing 4 weeks. Looking forward to my next co:rise class :)
I was so excited about this class, that I dropped my grad class that I was taking at the same time. One of the things that got me really excited is Andrew’s years in the field allowed him to take complicated concepts and simplify them. When Andrew talked about problem formulation, and running a smaller experiment, it was pivotal in giving me the confidence at work to propose a smaller solution, publish early, talk about our methods. Mentorship with the course team was AMAZING.
You can learn a lot in this course even if you don’t have much prior Python or ML experience as long as you are willing to put in some time on the projects
A crash course in learning the basics to get you started on building your first ML models while using state of the art techniques to further improve performance.
I would definitely recommend the Uplimit community as it provides a major incentive and community compared to most MOOCs - It's what you make of it, but if you're invested and put a lot in, you will get even more out
Started as a novice to machine learning I get to learn so much in just 4 weeks of course. Starting from basics to training state of the art ML models. The way projects are designed is exceptional and is very helpful in learning quick. Team is helpful and very prompt in responding to any queries.
This course is incredibly important and useful! I believe it should be required in any data-science curriculum. We gained practical skills to tackle problems that data scientists and machine learning engineers often face when dealing with real-world messy data. I learned so much more than the course material due to the encouragement and guidance of Mike Wu!
This final course in the ML track series provided a realistic framework bridging the concepts we have covered in all 3 classes into a more productionalized format. This course has given a real insight into what a real ML backend may look like and the steps required to get there.