Comparing Algorithm Efficiency | Time Complexity of Sorting, Searching & More

Not all algorithms are created equal — some are lightning-fast, while others crawl as input size grows. In this video, we compare the efficiency of various algorithms using time complexity (Big O notation) to help you understand which algorithm works best in different scenarios.

📚 What You'll Learn:

✅ How to measure algorithm efficiency
✅ Comparison of common algorithms:
  • Linear Search vs Binary Search
  • Bubble Sort vs Merge Sort vs Quick Sort
✅ Time complexities: O(1), O(log n), O(n), O(n log n), O(n²), etc.
✅ Graphical comparisons and real-world examples
✅ How input size affects performance (scalability)

Whether you're prepping for coding interviews, learning DSA, or just curious, this is a must-watch for understanding why algorithm choice matters.

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Видео Comparing Algorithm Efficiency | Time Complexity of Sorting, Searching & More канала Computer Tutorials