UNDERSTANDING ALGORITHM

[Audio] Understanding Algorithm By Tech & Learn Classroom.

[Audio] Definition: A step-by-step procedure for solving a problem or performing a task Importance: Algorithms are the foundation of computer science and programming Introduction to Algorithms.

[Audio] Input and Output: Defines the inputs and outputs Steps: Finite number of steps Effectiveness: Each step is simple and clear Features of an Algorithm.

[Audio] Example: Sorting numbers Steps: Breaking down the task into smaller steps, like comparing and swapping How Algorithms Work.

[Audio] Real-World Examples of Algorithms Examples Search engines G-P-S navigation Shopping recommendation systems.

[Audio] Types of Algorithms Overview Searching Algorithms Sorting Algorithms Divide and Conquer Greedy Algorithms Recursive Algorithms.

[Audio] Definition: Finding an element from a collection of data Types Linear Search Binary Search Real-world examples: Finding a name in a list, looking for files on your computer Searching Algorithms.

[Audio] Definition: A simple search algorithm that checks each element one by one Pseudocode: plaintext Copy code Algorithm Linear Search(array, target): for i = 0 to length(array) 1: if array[i] == target: return i return -1 Real-world Application: Searching for a contact in your phonebook. Linear Search Algorithm.

[Audio] Definition: A more efficient search algorithm that works on sorted data Pseudocode: Algorithm BinarySearch(array, target): low = 0 high = length(array) 1 while low <= high: mid = (low plus high) / 2 if array[mid] == target: return mid else if array[mid] < target: low = mid plus 1 else: high = mid 1 return –1 Real-world Application: Searching in a phone directory. Binary Search Algorithm.

[Audio] Definition: Arranging data in a specific order Types Bubble Sort Selection Sort Merge Sort Quick Sort Sorting Algorithms.

[Audio] Bubble Sort Algorithm Definition: A simple sorting algorithm that repeatedly steps through the list Pseudocode : plaintext Copy code Algorithm BubbleSort(array): for i = 0 to length(array) 1: for j = 0 to length(array) i –1: if array[j] > array[j plus 1]: swap(array[j], array[j plus 1]).

[Audio] Definition: A sorting algorithm that selects the minimum element from the unsorted part Pseudocode : plaintext Copy code Algorithm Selection Sort(array): for i = 0 to length(array) 1: min Index = i for j = i plus 1 to length(array): if array[j] < array[min Index]: min Index = j swap(array[min Index], array[i]) Selection Sort Algorithm.

[Audio] Definition: A divide-and-conquer sorting algorithm that splits the array and merges them after sorting. Merge Sort Algorithm.

[Audio] Merge Sort Algorithm pseudocode plaintext Copy code Algorithm Linear Search(array, target): for i = 0 to length(array) 1: if array[i] == target: return i return -1.

[Audio] Definition: A sorting algorithm that picks a 'pivot' and partitions the array around the pivot Pseudocode: plaintext Copy code Algorithm Quick Sort(array, low, high): if low < high: pi = Partition(array, low, high) Quick Sort(array, low, pi 1) Quick Sort(array, pi plus 1, high) Quick Sort Algorithm.

[Audio] Comparing Sorting Algorithms Algorithm Time Complexity Space Complexity Bubble Sort O(n²) O(1) Merge Sort O(n log n) O(n) Quick Sort O(n log n) O(log n).

[Audio] Definition: Break the problem into smaller parts, solve them recursively, and combine results Examples: Merge Sort, Quick Sort, Binary Search Divide and Conquer Algorithms.

[Audio] Definition: Make the locally optimal choice at each step Examples Coin change problem Huffman encoding Greedy Algorithms.

[Audio] Definition: Solve a problem by breaking it down into smaller instances of the same problem Example: Calculating the factorial of a number Pseudocode : plaintext Copy code Algorithm Factorial(n): if n == 0: return 1 else: return n * Factorial(n 1) Recursive Algorithms.

[Audio] Time Complexity: How the running time of an algorithm grows with input size Space Complexity: How much memory is required by the algorithm Big-O Notation: Expresses the upper bound of the algorithm's growth rate Complexity of Algorithms.

[Audio] O : Constant time O : Linear time O : Quadratic time O : Logarithmic time O : Linearithmic time Big-O Notation.

[Audio] Example of Time Complexity: Linear against Binary Search Linear Search: O Binary Search: O Comparison: Binary search is more efficient on large datasets.

[Audio] Goal: Improve time or space efficiency Methods Use better data structures Optimize loops and conditions Apply caching/memoization Algorithm Optimization.

[Audio] Easier to understand Abstracts language-specific details Flowcharts Visual representation of an algorithm Use decision points and arrows for logic flow Pseudocode and Flowcharts.

[Audio] Example of Algorithm Representation Problem: Find the largest number in an array Pseudocode: plaintext Copy code Algorithm Find Largest(array): largest = array[0] for i = 1 to length(array) 1: if array[i] > largest: largest = array[i] return largest..

[Audio] Search Engines: Page ranking algorithms Data Encryption: Algorithms for securing communication Social Media: Recommendation systems Practical Applications of Algorithms.

[Audio] Brute Force: Trying all possibilities Divide and Conquer: Break problems into smaller subproblems Greedy Algorithms: Make the best choice at each step Algorithm Design Paradigms.

[Audio] Scalability: How well does the algorithm handle large inputs? Efficiency: Balancing time and space complexity Correctness: Ensuring the algorithm always gives the correct result Challenges in Algorithm Design.

[Audio] : Example of Algorithm in Real Life Sorting a Playlist: Algorithms to sort your favorite songs in ascending or descending order.

[Audio] Q&A Questions: 1. What is an algorithm? Give an example. 2. In which case is a linear search more efficient than a binary search? 3. Write the pseudocode for a binary search algorithm. 4. Explain the difference between merge sort and quick sort in terms of their approach to sorting. 5. What is the main idea behind greedy algorithms? Provide a real-world example. Comment Down Below If You Know About It.

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