Coding Projects
前往频道在 Telegram
Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data
显示更多📈 Telegram 频道 Coding Projects 的分析概览
频道 Coding Projects (@programming_experts) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 67 937 名订阅者,在 技术与应用 类别中位列第 1 917,并在 印度 地区排名第 4 746 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 67 937 名订阅者。
根据 26 十月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 182,过去 24 小时变化为 -5,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 3.30%。内容发布后 24 小时内通常能获得 1.05% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 2 243 次浏览,首日通常累积 710 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 4。
- 主题关注点: 内容集中在 |--, algorithm, array, framework, javascript 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台:
“Channel specialized for advanced concepts and projects to master:
* Python programming
* Web development
* Java programming
* Artificial Intelligence
* Machine Learning
Managed by: @love_data”
凭借高频更新(最新数据采集于 27 十月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。
67 937
订阅者
-524 小时
-227 天
+18230 天
帖子存档
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67 940
An LRU Cache removes the item that has not been used for the longest time when the cache reaches its capacity.
A common implementation uses:
• Hash Map for O(1) lookup.
• Doubly Linked List for O(1) insertion and removal.
Time Complexity:
• Get: O(1)
• Put: O(1)
2️⃣0️⃣0️⃣ How Would You Design a URL Shortener?
Answer:
A URL shortener converts a long URL into a short, unique URL.
Example:
Long URL: https://example.com/products/category/item/12345
Short URL: https://short.ly/aB92x
A basic system can use:
1. Generate a unique ID for each URL.
2. Convert the ID into a short Base62 string.
3. Store the mapping between the short code and original URL.
4. When the short URL is requested, look up the original URL.
5. Redirect the user to the original URL.
Important Design Considerations:
• Unique short IDs
• Database design
• Caching
• Scalability
• High availability
• Expiration of URLs
• Analytics and click tracking
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-----
2.3 ₽ · /balance_help
67 940
🚀 Coding Interview Questions with Answers (Part 20)
1️⃣9️⃣1️⃣ What is the Two Sum Problem?
Answer:
The Two Sum problem asks you to find two elements in an array whose sum equals a given target.
Example:
Input: [2][7][11][15]
Target: 9
Output: [2][7]
A Hash Map can be used to store previously seen values and find the required complement efficiently.
Time Complexity: O(n)
Space Complexity: O(n)
1️⃣9️⃣2️⃣ What is the Longest Substring Without Repeating Characters Problem?
Answer:
The goal is to find the longest substring that contains no repeated characters.
Example:
Input: "abcbb"
Output: 3
The longest substring is "abc".
A Sliding Window with a Hash Set or Hash Map can solve this efficiently.
Time Complexity: O(n)
Space Complexity: O(k)
1️⃣9️⃣3️⃣ What is the Longest Common Subsequence (LCS) Problem?
Answer:
LCS finds the longest sequence that appears in the same order in two strings, but the characters do not need to be adjacent.
Example:
Input: "abcde" and "ace"
Output: "ace"
Dynamic Programming is commonly used to solve this problem.
Time Complexity: O(m × n)
Space Complexity: O(m × n)
1️⃣9️⃣4️⃣ What is the Longest Increasing Subsequence (LIS) Problem?
Answer:
LIS finds the longest subsequence of an array where the elements are in strictly increasing order.
Example:
Input: [10][9][2][5][3][7][101][18]
Output: 4
One possible LIS is: [2][3][7][101]
It can be solved using Dynamic Programming or an optimized Binary Search approach.
Time Complexity: O(n log n) using the optimized approach.
1️⃣9️⃣5️⃣ What is the Maximum Subarray Sum Problem?
Answer:
The goal is to find the contiguous subarray with the largest possible sum.
Example:
Input: [-2][1][-3][4][-1][2][1][-5][4]
Output: 6
The maximum-sum subarray is: [4][-1][2][1]
Kadane's Algorithm can solve this efficiently.
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣9️⃣6️⃣ What is the Merge Intervals Problem?
Answer:
The Merge Intervals problem requires combining overlapping intervals into a single interval.
Example:
Input: [[1,3][2,6][8,10][9,12]]
Output: [[1,6][8,12]]
The typical approach is to sort the intervals by their starting value and then merge overlapping intervals.
Time Complexity: O(n log n)
Space Complexity: O(n)
1️⃣9️⃣7️⃣ What is the Trapping Rain Water Problem?
Answer:
The problem asks you to calculate how much rainwater can be trapped between bars of different heights.
Example:
Input: [0][1][0][2][1][0][1][3][2][1][2][1]
Output: 6
A Two Pointers approach can solve this problem efficiently by tracking the maximum height from both sides.
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣9️⃣8️⃣ What is the Median of Two Sorted Arrays Problem?
Answer:
The goal is to find the median of two sorted arrays without necessarily merging them completely.
Example:
Input: [1][3] and [2]
Output: 2
An optimized solution uses Binary Search to partition the two arrays correctly.
Time Complexity: O(log(min(m,n)))
Space Complexity: O(1)
1️⃣9️⃣9️⃣ What is the LRU Cache Problem?
Answer:
LRU stands for Least Recently Used.
67 940
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Time Complexity: O(n + m)
Space Complexity: O(n + m)
1️⃣8️⃣9️⃣ How Do You Check if Two Strings are Anagrams?
Answer:
Two strings are anagrams if they contain the same characters with the same frequencies, but possibly in a different order.
Example:
"listen" → "silent"
Both contain the same characters, so they are anagrams.
Python:
str1 = "listen"
str2 = "silent"
if sorted(str1) == sorted(str2):
print("Anagrams")
else:
print("Not Anagrams")
Time Complexity: O(n log n)
A frequency-count approach can achieve O(n) average time.
1️⃣9️⃣0️⃣ How Do You Find the First Non-Repeating Character?
Answer:
Count the frequency of every character, then scan the string again and return the first character whose frequency is "1".
Example:
Input: "swiss"
Output: "w"
Python:
from collections import Counter
text = "swiss"
count = Counter(text)
for char in text:
if count[char] == 1:
print(char)
break
Time Complexity: O(n)
Space Complexity: O(k), where "k" is the number of distinct characters.
🔥 Double Tap ❤️ For Part-20
-----
2.47 ₽ · /balance_help67 940
🚀 Coding Interview Questions with Answers (Part 19)
1️⃣8️⃣1️⃣ How Do You Reverse a String?
Answer:
Reversing a string means arranging its characters in the opposite order.
Example:
Input: "hello"
Output: "olleh"
Python:
text = "hello"
reversed_text = text[::-1]
print(reversed_text)
Time Complexity: O(n)
Space Complexity: O(n)
1️⃣8️⃣2️⃣ How Do You Find the Largest Element in an Array?
Answer:
Traverse the array while keeping track of the largest value found so far.
Example:
Input: [10, 25, 7, 42, 18]
Output: 42
Python:
numbers = [10, 25, 7, 42, 18]
largest = numbers[0]
for num in numbers:
if num > largest:
largest = num
print(largest)
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣3️⃣ How Do You Find the Second Largest Element in an Array?
Answer:
Maintain two variables: one for the largest element and another for the second largest. Update them while traversing the array.
Example:
Input: [10, 25, 7, 42, 18]
Output: 25
Python:
numbers = [10, 25, 7, 42, 18]
largest = second = float('-inf')
for num in numbers:
if num > largest:
second = largest
largest = num
elif largest > num > second:
second = num
print(second)
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣4️⃣ How Do You Check Whether a String is a Palindrome?
Answer:
A palindrome is a string that reads the same forward and backward.
Examples:
"madam" → Palindrome
"level" → Palindrome
"hello" → Not a palindrome
Python:
text = "madam"
if text == text[::-1]:
print("Palindrome")
else:
print("Not a palindrome")
Time Complexity: O(n)
1️⃣8️⃣5️⃣ How Do You Find Duplicate Elements in an Array?
Answer:
Use a set to keep track of elements that have already appeared. If an element is already present in the set, it is a duplicate.
Example:
Input: [1, 2, 3, 2, 4, 1]
Output: [1, 2]
Python:
numbers = [1, 2, 3, 2, 4, 1]
seen = set()
duplicates = set()
for num in numbers:
if num in seen:
duplicates.add(num)
else:
seen.add(num)
print(duplicates)
Average Time Complexity: O(n)
Space Complexity: O(n)
1️⃣8️⃣6️⃣ How Do You Remove Duplicates from an Array?
Answer:
A common approach is to use a set, which stores only unique values.
Example:
Input: [1, 2, 2, 3, 3, 4]
Output: [1, 2, 3, 4]
Python:
numbers = [1, 2, 2, 3, 3, 4]
unique_numbers = list(set(numbers))
print(unique_numbers)
If the original order must be preserved:
unique_numbers = list(dict.fromkeys(numbers))
Average Time Complexity: O(n)
1️⃣8️⃣7️⃣ How Do You Find the Missing Number in an Array?
Answer:
If an array contains numbers from "1" to "n" with one number missing, calculate the expected sum and subtract the actual sum.
Example:
Input: [1, 2, 4, 5]
Output: 3
Python:
numbers = [1, 2, 4, 5]
n = 5
expected = n * (n + 1) // 2
missing = expected - sum(numbers)
print(missing)
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣8️⃣ How Do You Merge Two Sorted Arrays?
Answer:
Use two pointers to compare elements from both arrays and add the smaller element to the result.
Example:
Input:
[1, 3, 5]
[2, 4, 6]
Output:
[1, 2, 3, 4, 5, 6]
Python:
a = [1, 3, 5]
b = [2, 4, 6]
i = j = 0
result = []
while i < len(a) and j < len(b):
if a[i] < b[j]:
result.append(a[i])
i += 1
else:
result.append(b[j])
j += 1
while i < len(a):
result.append(a[i])
i += 1
while j < len(b):
result.append(b[j])
j += 1
print(result)67 940
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If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
USEFUL RESOURCES TO LEARN MACHINE LEARNING
👇👇
Intro to ML by MIT Free Course
https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about
Machine Learning for Everyone FREE BOOK
https://buildmedia.readthedocs.org/media/pdf/pymbook/latest/pymbook.pdf
ML Crash Course by Google
https://developers.google.com/machine-learning/crash-course
Advanced Machine Learning with Python Github
https://github.com/PacktPublishing/Advanced-Machine-Learning-with-Python
Practical Machine Learning Tools and Techniques Free Book
https://vk.com/doc10903696_437487078?hash=674d2f82c486ac525b&dl=ed6dd98cd9d60a642b
Python Machine Learning for beginners
https://t.me/datasciencefun/1177?single
https://topmate.io/coding/914624
All the best 👍👍
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🚀 𝟰 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲🔥
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🚀 𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 | 𝟱 𝗠𝘂𝘀𝘁-𝗧𝗮𝗸𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🔥
Artificial Intelligence is transforming every industry—and now you can learn directly from Google with 100% FREE AI courses!
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1️⃣7️⃣9️⃣ What is DNS?
Answer:
DNS (Domain Name System) translates human-readable domain names like google.com into IP addresses that computers use to locate each other on the internet.
Benefits:
✅ Makes websites easier to access
✅ Eliminates the need to remember IP addresses
✅ Enables efficient internet communication
1️⃣8️⃣0️⃣ What is a CDN?
Answer:
A CDN (Content Delivery Network) is a network of geographically distributed servers that deliver website content from the server closest to the user.
Benefits:
✅ Faster website loading
✅ Reduced latency
✅ Lower server load
✅ Improved availability and reliability
✅ Better user experience for global audiences
Examples: Cloudflare, Akamai, Amazon CloudFront, Google Cloud CDN
🔥 Double Tap ❤️ For Part-19
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