Machine Intelligence - an Introductory Course
- Understanding of Calculus and Linear Algebra will help better understand most of the concepts discussed here. But you can look for helpful resources alongside studying this course.
This course focuses on the theoretical aspects of the field of Data Science and Machine Learning. It helps the students to quickly gain an in-depth overview of different algorithmic techniques used in various domains and applications. This course features external links to further enhance the experience and reinforce the concepts acquired. It also provides easy explanations of popular and useful research papers that are driving this field forward.
Who this course is for:
- Aspiring and Professional Data Scientists and Machine Learning Engineers.
- Students pursuing their PhD and looking for a refresher course.
- 02:38Introduction to Survival Analysis
- 02:36The Survival Function and the Hazard Function
- 03:09Kaplan-Meier Estimate and Nelson Aalen Fitter
- 06:39Survival Regression - Cox Proportional Hazard Regression Model
I am a Machine Learning Engineer, with three years of experience in the field of Data Science and Machine Learning. I am a Former Teaching Assistant for the Deep Learning Master's Degree Course and the Natural Language Processing Course. I have worked on a wide range of projects including, but not limited to, Real-time Vehicle Detection and Tracking, Financial Time-Series Forecasting, and Anomaly Detection in Images.
My goal with these courses is to help you stand out in the field of Data Science and Engineering. I follow the agile development methodologies to design, create, and publish my courses. I follow incremental sprints to update my courses regularly by either adding new content to existing courses or creating an entirely new course. This allows me to not only respond to and structure my courses based on direct student feedback, but also, to add the latest skill in demand as quickly as possible.