FAQ

The duration of recorded course will be 12 months with some variability as we are offering it for the first time. You will have access to the course for 36 months. After that it might be available but we make no promises of lifetime access. 

For recorded courses, there is no refund. 

See our refund policy page ( https://thecuriouscurator.in/refund_returns/ ).  

The course starts with basics including python but ramps up quickly towards the a DS-ML Programme. If you have some familiarity with the space, you would get 10x value out of it. To excel in the Programme, you will need to have

  • A good internet connection for zoom calls and video streaming.
  • Have a commitment of 12-months of time over weekends that you can dedicate towards upskilling.
  • A few hours every week for revision of past content covered in the Programme or playing around with data and libraries. 
  • You have at least high school level (class 12 equivalent) of mathematics exposure. Having exposure to bachelors level mathematics is helpful but not required. 
  • Although we will cover everything  required to proceed but exposure to programming in any language is desirable. We will use python  language throughout the course.
  • You are interested in learning real world scenarios in applied scientists job.
  • You are interested to understand the foundations of ML to have a rewarding career in this space. 
  • Most importantly, willingness to work hard. 

You can find the details at our flagship course here. 

Few salient points below

  • The program will have a blend of Recorded Classes and doubt clearing sessions on Weekends, Group AMA Sessions, and self-assignments. 
  • Focus on Computational Probability & Statistics
  • Applied Scientist Interview Perspectives
  • Learn From Industry Practitioner
  • Large Industry Cases (real Ads & Recommendation system), ML Design & Select Research Papers
  • Code Walkthrough to Reinforce Concepts
  • A really long list of ML algorithms
  • Few advanced topics like transformers, variational inference, low variance estimators, diffusion models, counterfactual estimation & learning. Justification of subtle things like why spectral clustering partitions graphs etc.