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Incoming graduate student at Harvard University
“The Univ.AI Foundation course gave me a structured learning environment. They helped me understand why one algorithm worked better than another for a given application. The quality of the peer group and the personalized time devoted by the professors are the two things that surprised me”
Phd Mathematics, Technical University, Munich
“The learning experience at Univ.AI is highly engaging, interactive and lively. It is most definitely on par with the best universities in the world. I enjoyed the teaching style of Dr. Protopapas and the high level of care and dedication displayed by the academic team.”
Data Scientist at Glance, Ex- VMware
“My growth has been tremendous! I see a huge difference in the quality of code I write now compared to what I used to write in VMware. My new role requires me to pick up new tools quickly, and I think it’s because of my training at Univ.AI that I can adapt with great ease. Univ.AI helped me refine my concepts, and I think their program is the perfect training for any data scientist role.”
Machine Learning Lead at Metro Services
“The mentorship we got during the program was a perfect complement to learning from top faculty. The curriculum is challenging, but for the committed students the learning experience is exceptional. I was surprised at the pace at which I was able to develop my expertise.”
Software Engineer, Larsen & Toubro Infotech
“One of the things that made a big difference for me was learning with a smart and highly accomplished peer group. My classmates would often ask questions that I hadn’t thought of. Peer learning added greatly to an already inspiring learning experience.”
This course starts you off from scratch, with the basics of python programming, including python data structures, functions and classes. We follow this up by an introduction to Numerical Python (NumPy) and linear regression. Along the way, we will introduce foundational ideas of statistics, linear algebra and calculus.
At the end of this course, you will have the tools and the concepts needed to successfully undertake a rigorous course in Data Science and AI.
Topics covered in AI-0: The Basics of Data Science (formerly called PyDS) are pre-requisites for our Master ML & AI program or its first course [AI-1] AI-Basics
This part-time, weekend only course is for college or university students and working professionals who seek a career in Machine Learning and AI, but do not have the requisite knowledge of python programming, basic statistics and mathematics.
Go deeper into what you will learn in each session in this 5-weekend course
Introduction to Python Programming
Python Data Structures, Flow Control
Reading & Writing Files
Code Debugging, Third-party Modules
Probability & Statistics
Statistics with NumPy
Linear Algebra, Calculus and Linear Regression