Diagram of O(1) or constant algorithm

GN guys! see you morning

GM Fam🌅

The next point is O(n): If an operation has an O(n) (pronounced ‘O of N’), what this means is that, as ’n’ increases, so does the amount of operations needed to complete the problem. This is simple to understand, cuz it’s an entirely linear relationship. Here O(n) code looks like

def findNum(n):
for i in range(n):
print(i)

findNum(5)

To say it's linear Big O notation: Look at there is only one for loop it's not nested. when our n = 5 the time to complete this operation is also increase, and if n = 100 the time to complete this operation is also further increases, 👉🏾that's why O(1) is preferably than O(n). and then the graph of O(n) is👇

Good Evening Guys🌃
I wish u're doing well and we'll keep learning without any stress💪Thanks for being with me🙏

What we've to understand is: An O(n) operation inside of an O(n) operation is an O(n * n) operation. In other words, O(n ²). This is the slowest and least efficient, and therefore the least desirable Big O Expression when considering time complexity. Here eg; of O(n ²)

def calc(n, m):
for i in range(n):
for j in range(m):
print(i, j)
calc(4, 5)

Since it's nested loop it's O(n ²) algorithm,

List: List is a built-in dynamic array which can store elements of different data types. It is an ordered collection of item, that is elements are stored in the same order as they were inserted into the list. List stores references to the objects (elements) rather than storing the actual data itself.

b = [2, 4, "word", True]
print(b)

a = list((1, 4, 3.2, 'banana', 5))
print(a)
b = list('STC')
print(b)
# Creating list with repeated elements
# we can use * for repeated items
a = [3] * 3
print(a)

# Accessing list elements
# elements in a list are accessed using indexing.
# Python indexes start at 0
a = [12,13,15,18]
print(a[0])
print(a[1])
print(a[2])
print(a[1:-1])

Good Morning Family🌅

Key concept we've to know👇

#Adding elements into list
"""we can add elements to a list using the following methods:
append(): add at the end of the list
extend(): adds multiple elements to the end of the list
insert(): adds at a specific position
clear(): removes all items"""
x = []
x.append(15)
x.append(2)
x.append(3)
print("After append:", x)

x.insert(0, 1)
print("After insert", x)

x.extend([4, "list", True])
print("After extend", x)

x.clear()
print("After clear:", x)

Don't worry this course might be confusion, however what we learn in this course is very interesting and important! you've to use different resourses, we're on our way to understand, analysis, solve problem, but we must've to consistence 💪

Good Morning Fam🌅

Python list looks like this👆 and the first box is index of 0 then 1,2,3,4 to understand list of index we can calculate like this index = length - 1. eg; a[0] = 10, a[1] = 20, a[2] =' GfG', a[3] = 40, a[4] = 'True' questions what's a[-1] your answer in the comment sections↩️

here I've prepared the topics we'll going to focus one by one

DATA STRUCTURE:
Array DS, String in DS, Set, Dictionary, Recursion, Linked List DS, Stack DS, Queue DS, hash in DS, Tree DS, Heap in DS, Graph DS, Trie DS

ALGORITHMS: Searching Algorithms, Sorting Algorithms, Tree Traversal, Graph Algorithms,

PROBLEM SOLVING TECHNIQUES: Two Pointers, Sliding Window, Recursion, Dynamic Programming(DP), Greedy

May be this course takes time don't worry we'll learn from each others, ask questions, and share info. Let's go together and become mastered in DSA💪

Searching Algorithm: There are 2 types of SA 1. Linear Search & 2. Binary Search from them here; 1. Linear Search in diagram in easy way👆 let me know what you've understand from this 👀

Good Night😴

Good Morning Guys🌄
We'll not stop our learning journey to ML