The Applied Course is an Ed-Tech organization that focuses on teaching students who are preparing for the GATE Computer Science and Information Technology Exam and also helping the students getting placed in Product Based Companies by making them prepare for the interviews. Our main focus is to make students learn the concepts in detail from basic till advanced.
The course content is developed by a team of 15+ mentors from top IISc, IITs, IIITs, NITs, etc. after a lot of research. This course will focus on practical knowledge more than theoretical rigor. That doesn't mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of computer science as the course name suggests. Through the course, we will be providing you with 900+ practice questions so that the students can self assess themselves after finishing every 10-15 hours of content. For each idea/algorithm, we would provide examples
the math of time complexity is wrong is should be ((n+2)(n-1))/2 not n(n+1)/2 you can dry run the algo yourself and compare the answers with both equations yourself
Thank you! I couldn't understand it in other people's videos, but this was very descriptive. You have a logic-based approach that is far from memorized.
I want to ask that the swapping and the decrement of i will be one less then the time while loop is executing so we should minus 1 in 6th and 7th step in worst case.
I have question about 4:21 about drawing recursion tree. I always draw them like for each node, calculating the work done in this node. (as shown in Introduction to Algorithms 4th Leiserson Stein Rivest Cormen MIT Press p. 96) using this approach the recursion tree drawn should be n ---> cn, n-1--->c(n-1), n-2 ---> c(n-2), ... 1 ---> 1 but you draw it slightly different. I guess both approach gives the correct results for the example in p. 96 but i am not sure. Can you explain it a bit further?
Is the assumption "all" m<n is correct as by definition big-oh states n >= n0? Hence might there exist m < n0 where the condition does not hold? Perhaps "some" m<n is more accurate where n0 <= m < n. You can then set n > 2 * n0 and then let m be 1/2 n. Assuming the lower bound of m is n0, the rest should follow.
Man I wish I had seen this prior to my algorithms midterm exam. The prof just uses the examples from the textbook, but the way you explain the steps make it so much clearer to understand. I will definitely be using you as a resource from now on
