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.
- Aspiring and Professional Data Scientists and Machine Learning Engineers.
- Students pursuing their PhD and looking for a refresher course.
- Introduction to Survival Analysis02:38
- The Survival Function and the Hazard Function02:36
- Kaplan-Meier Estimate and Nelson Aalen Fitter03:09
- Survival Regression - Cox Proportional Hazard Regression Model06:39
- Introduction to Information Retrieval02:27
- Text Preprocessing04:31
- Term-Document Incidence Matrix01:55
- Inverted Index05:30
- Retrieval Vector Space Model06:54
About Me: I am a Machine Learning Engineer, with over two 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 a Bachelor's Degree in Computer Science, Nanodegrees in Deep Learning and Artificial Intelligence, and a keen interest in all things Data Science.
My Courses: I follow the agile development methodologies to design, create, and publish my courses. I follow small manageable 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.