Name: UNDERSTANDING ALGORITHM
Uploaded: 2024-10-20
Duration: 8 min 24 s
Description: UNDERSTANDING ALGORITHM BY TECH & LEARN CLASSROOM.

[Audio] Understanding Algorithm By Tech & Learn Classroom. 

Scene 1

[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. 

Scene 2

Definition: A step-by-step procedure for solving a problem or performing a task

 Importance: Algorithms are the foundation of computer science and programming

[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. 

Scene 3

Input and Output: Defines the inputs and outputs

 Steps: Finite number of steps

 Effectiveness: Each step is simple and clear

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

Scene 4

Example: Sorting numbers

 Steps: Breaking down the task into smaller steps, like comparing and swapping

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

Scene 5

Examples Search engines GPS navigation Shopping recommendation systems

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

Scene 6

Searching Algorithms Sorting Algorithms Divide and Conquer Greedy Algorithms Recursive Algorithms

[image] Vibrant multicolour checkered floor design

[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. 

Scene 7

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

[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. 

Scene 8

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.

[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. 

Scene 9

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 + high) / 2   if array[mid] == target:  return mid  else if array[mid] < target:  low = mid + 1  else: high = mid - 1    return –1 

 Real-world Application: Searching in a phone directory.

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

Scene 10

Definition: Arranging data in a specific order

 Types

 Bubble Sort

 Selection Sort

 Merge Sort

 Quick Sort

[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]). 

Scene 11

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 + 1]:
                swap(array[j], array[j+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. 

Scene 12

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 + 1 to length(array): 
           if array[j] < array[min Index]: 
               min Index = j 
       swap(array[min Index], array[i])

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

Scene 13

Definition: A divide-and-conquer sorting algorithm that splits the array and merges them after sorting.

[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. 

Scene 14

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. 

Scene 15

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 + 1, high)

[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). 

Scene 16

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. 

Scene 17

Definition: Break the problem into smaller parts, solve them recursively, and combine results

 Examples: Merge Sort, Quick Sort, Binary Search

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

Scene 18

Definition: Make the locally optimal choice at each step

 Examples

 Coin change problem

 Huffman encoding

[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. 

Scene 19

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)

[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. 

Scene 20

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

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

Scene 21

O : Constant time

 O : Linear time

 O : Quadratic time

 O : Logarithmic time

 O : Linearithmic time

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

Scene 22

Example of Time Complexity: Linear vs. 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. 

Scene 23

Goal: Improve time or space efficiency

 Methods

 Use better data structures

 Optimize loops and conditions

 Apply caching/memoization

[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. 

Scene 24

Easier to understand

 Abstracts language-specific details

 Flowcharts

 Visual representation of an algorithm

 Use decision points and arrows for logic flow

[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.. 

Scene 25

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. 

Scene 26

Search Engines: Page ranking algorithms

 Data Encryption: Algorithms for securing communication

 Social Media: Recommendation systems

[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. 

Scene 27

Brute Force: Trying all possibilities

 Divide and Conquer: Break problems into smaller subproblems

 Greedy Algorithms: Make the best choice at each step

[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. 

Scene 28

Scalability: How well does the algorithm handle large inputs?

 Efficiency: Balancing time and space complexity

 Correctness: Ensuring the algorithm always gives the correct result

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

Scene 29

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. 

Scene 30

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

[image] Rear view of rows of people watching a film in a theater

[Audio] Thank You for any queries comment below don’t forget to subscribe to my channel. 

Scene 31

Thank You  for any queries comment  below don’t forget to subscribe to my channel

UNDERSTANDING ALGORITHM

Understanding Algorithm


 By Tech & Learn Classroom

avatar

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

Input and Output: Defines the inputs and outputs

 Steps: Finite number of steps

 Effectiveness: Each step is simple and clear


  Features of an Algorithm

Example: Sorting numbers

 Steps: Breaking down the task into smaller steps, like comparing and swapping


  How Algorithms Work

Real-World Examples of Algorithms


 Examples Search engines G-P-S navigation Shopping recommendation systems

Types of Algorithms Overview
 Searching Algorithms Sorting Algorithms Divide and Conquer Greedy Algorithms Recursive Algorithms

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

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

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

Definition: Arranging data in a specific order

 Types

 Bubble Sort

 Selection Sort

 Merge Sort

 Quick Sort


  Sorting Algorithms

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])

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

Definition: A divide-and-conquer sorting algorithm that splits the array and merges them after sorting.


  Merge Sort Algorithm

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

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

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)

Definition: Break the problem into smaller parts, solve them recursively, and combine results

 Examples: Merge Sort, Quick Sort, Binary Search




  Divide and Conquer Algorithms

Definition: Make the locally optimal choice at each step

 Examples

 Coin change problem

 Huffman encoding


  Greedy Algorithms

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

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

O : Constant time

 O : Linear time

 O : Quadratic time

 O : Logarithmic time

 O : Linearithmic time


  Big-O Notation

Example of Time Complexity: Linear against Binary Search


 Linear Search: O Binary Search: O Comparison: Binary search is more efficient on large datasets

Goal: Improve time or space efficiency

 Methods

 Use better data structures

 Optimize loops and conditions

 Apply caching/memoization


  Algorithm Optimization

Easier to understand

 Abstracts language-specific details

 Flowcharts

 Visual representation of an algorithm

 Use decision points and arrows for logic flow


  Pseudocode and Flowcharts

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.

Search Engines: Page ranking algorithms

 Data Encryption: Algorithms for securing communication

 Social Media: Recommendation systems




  Practical Applications of Algorithms

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

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

: Example of Algorithm in Real Life


 Sorting a Playlist: Algorithms to sort your favorite songs in ascending or descending order

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