Dynamic Programming Made Easy A Step-by-Step Beginner GuidePublished 9/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle |
Duration: 5h 5m |
Size: 2.57 GB
The Hidden Logic for Solving Any Dynamic Programming Problem
What is Dynamic Programming?
What is Memoization?
How Many Types of Dynamic Programming Actually Exist?
What is Linear DP?
How to solve Fibonacci Numbers?
How to solve Climbing Stairs?
How to solve House Robber?
How to solve Jump Game?
What is Kadane's Algorithm?
How to solve Maximum Subarray?
How to solve Maximum Circular Subarray Sum?
How to solve Maximum Product Subarray?
How to find Best Time to Buy and Sell Stock?
What is 0/1 Knapsack?
How to solve Classic 0/1 Knapsack?
How to solve Subset Sum?
How to solve Partition Equal Subset Sum?
How to solve Target Sum?
What is Unbounded Knapsack?
How to solve Classic Unbounded Knapsack?
How to solve Coin Change?
How to solve Coin Change II?
How to solve Rod Cutting?
What is Subsequence?
How to solve Longest Common Subsequence?
How to solve Edit Distance?
How to solve Longest Palindromic Subsequence?
How to solve Distinct Subsequences?
RequirementsYou should have an IDE (Eclipse, Intellij, etc) for coding
You should know the basics of Java Programming
DescriptionReady to conquer complex problems? This comprehensive
Dynamic Programming course is your ultimate guide! We're diving deep into one of computer science's most powerful techniques.
Let's start with a fundamental question: Why should you care about dynamic programming? The answer lies in efficiency. On one side, we have *
Dynamic Programming* - a smart approach that significantly reduces redundant calculations. It optimizes both performance and feasibility by breaking down complex problems into simpler subproblems. On the other side, we have *
Brute Force* methods, which can lead to exponential time complexity. This makes them impractical for larger problem instances because they involve exhaustive searching and lead to redundant calculations.
Think of it this way: Would you rather solve a puzzle by trying every possible combination, or by remembering which pieces you've already tried? Dynamic programming is like having a perfect memory for problem-solving.
By the end of this course, you'll not only understand *
why* Dynamic Programming is a game-changer but also
how to implement it effectively, step-by-step, with ease.
What you'll learn in this series
- Introduction to Dynamic Programming & Memoization
- Common DP types with real-world examples
- Linear DP
- Fibonacci Numbers
- Climbing Stairs
- House Robber
- Jump Game
- Kadane's Algorithm
- Maximum Subarray
- Maximum Circular Subarray Sum
- Maximum Product Subarray
- Best Time to Buy and Sell Stock
- 0/1 Knapsack
- Classic 0/1 Knapsack
- Subset Sum
- Partition Equal Subset Sum
- Target Sum
- Unbounded Knapsack
- Classic Unbounded Knapsack
- Coin Change
- Coin Change II
- Rod Cutting
- Subsequence
- Longest Common Subsequence
- Edit Distance
- Longest Palindromic Subsequence
- Distinct Subsequences
Whether you're a beginner or looking to solidify your knowledge, this series is for YOU!
Who this course is forDevelopers curious to learn about Dynamic Programming
Java developers willing to learn how to solve DP problems
Anyone pursuing career in backend development
Beginners looking forward to understand the different types of Dynamic Programming
Anyone pursing a high paying job in IT Industry
Anyone targeting for the FAANG companies
Developers willing to clear job interviews with ease
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