Learn the fundamentals and advanced applications of Dynamic Programming in this comprehensive 3.5-hour tutorial. Explore essential concepts, including the definition and implementation of Dynamic Programming in data structures and algorithms. Dive into practical problem-solving with examples such as the Coin Change Problem, addressing both the number of ways to achieve a total and finding the minimum number of coins required. Tackle the Traveling Salesman Problem and understand its solution using Dynamic Programming. Master the 0/1 Knapsack Problem and compare it with the Fractional Knapsack Problem solved through the Greedy Method. Discover techniques for solving the Subset Sum Problem and uncover the intricacies of finding the Longest Common Subsequence. Enhance your algorithmic skills and problem-solving abilities through this in-depth exploration of Dynamic Programming techniques.