0/1 Knapsack Problem Made Easy!! with Dynamic Programming (DP) + SOLVED Example ✅ #algorithm

🚀 Knapsack Problem with Dynamic Programming! Master the 0/1 Knapsack Problem! In this video we are covering the 0/1 knapsack and explain the DP approach step by step, solve an example, explain how to optimize item selection for maximum value while staying within weight constraints. Learn dynamic programming techniques like bottom-up DP table, and memoization to efficiently solve knapsack algorithm problems. Watch as I visualize the DP table, explain time complexity, and implement the formula for better understanding. Perfect for LeetCode practice, coding interviews, competitive programming, and DSA exams like FAANG, Google, Amazon, and ACM ICPC.

💡 What You’ll Learn:
✔️ Knapsack Problem explained from scratch.
✔️ How to solve Knapsack Using Dynamic Programming (DP).
✔️ Knapsack Problem Step-by-step problem-solving approach.
✔️ Time Complexity of Knapsack Problem with and without Dynamic Programming (DP).

👨‍💻 Watch this video if you are preparing for:
Your Computer Science (CS) Exams
Understanding algorithms and data structures.
LeetCode Dynamic Programming (DP) problems.
Coding interviews (FAANG, Google, Amazon, Microsoft).
Competitive programming contests (Codeforces, Atcoder, Hackerrank).

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📣 Have questions? Drop them in the comments! Let’s solve it together! 💪

#knapsack #DynamicProgramming #LeetCode
#KnapsackProblem #DataStructures #Algorithms #CodingInterview #CompetitiveProgramming #Python #DSA #TechEducation #ComputerScience
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I would calculate all the item’s worth based on the value/weight and get a value for each and then choose a combination that doesn’t get over the limit but has the highest worthy items