Coding & AI Resources
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“📚Get daily updates for :
✅ Free resources
✅ All Free notes
✅ Internship,Jobs
and a lot more....😍
📍Join & Share this channel with your friends and college mates ❤️
Managed by: @love_data
Buy ads: https://telega.io/c/leadcoding”
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35 424
𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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35 424
🔅 Voice Recorder in Python
pip install sounddevice
import sounddevice
from scipy.io.wavfile import write
#sample_rate
fs=44100
#Ask to enter the recording time
second = int(input("Enter the Recording Time in second: "))
print("Recording…\n")
record_voice = sounddevice.rec(int(second * fs),samplerate=fs,channels=2)
sounddevice.wait()
write("MyRecording.wav",fs,record_voice)
print("Recording is done Please check you folder to listen recording")
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𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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🌻 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗕𝗶𝗴 𝗢 𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻!
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(n²) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
35 424
𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries
35 424
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35 424
Tips for solving leetcode codings interview problems
If input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
