In this module, we solve several real-world problems using dynamic programming. You’ll tackle problems such as the "Staircase Problem," "Rod Cutting," and "Share Market," each highlighting how DP improves problem-solving efficiency.

This module focuses on the Longest Increasing Subsequence (LIS) and related problems. You’ll explore how dynamic programming helps solve LIS, track compatible pairs in the Longest Chain of Pairs, and solve the Envelope Nesting problem through DP.

In this module, we explore advanced DP techniques to solve string-based problems, including finding the LCS, calculating the minimum edit distance between strings, and solving the longest palindromic subsequence problem. These concepts are crucial for understanding how DP can optimize string comparison tasks.

This module focuses on dynamic programming applications for stock trading. You will explore how to maximize profits by buying and selling stocks with constraints such as transaction fees and cooldown periods, optimizing solutions for various trading scenarios.

In this module, we will explore dynamic programming solutions for 2D grid-based problems. From calculating unique paths in a grid to handling obstacles, you will learn how to use DP to solve pathfinding and optimization problems in matrix-like structures.

In this module, we focus on tree-based dynamic programming problems. You’ll learn how to use DP to solve problems like the House Robber in Binary Tree, maximizing the profit without alerting the police, and how to find the maximum path sum in a binary tree.