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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

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ML Crash Course by Google

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Advanced Machine Learning with Python Github

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Practical Machine Learning Tools and Techniques Free Book

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🚀 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)

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.

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🚀 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.

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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Build real AI products - not just prompts

🎯 Program Highlights:-

🚀 15+ AI Projects
👨‍🏫 Live Online Classes + 1-on-1 Mentorship
💼 End-to-End Placement Support
🤝 500+ Partner Companies
🎓 2000+ Students Placed
💰 Average Salary: ₹7.4 LPA
🏆 Highest Salary: ₹41 LPA

🔗 𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀:-

https://pdlink.in/4fWJVID

🔥 Learn AI → Build Real Projects → Create Your Portfolio → Become Job Ready

📊 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 🚀

Want to start a career in Data Analytics & Business Intelligence? Learn Power BI through Microsoft learning modules and build practical, job-relevant analytics skills.

🎯 Perfect for Students | Freshers | Data Analyst Aspirants | Working Professionals

🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

https://pdlink.in/4zhGTX6

🔥 Start learning Power BI and turn raw data into powerful business insights!

📊 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 | 𝟱 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 🚀

Learning Data Analytics? Don't stop with tutorials — build real projects that you can showcase on your resume and portfolio! 💻

🔥 Practice with 5 Hands-On Projects covering:

🗄️ SQL
📊 Excel
📈 Tableau
📉 Power BI

🔗𝗟𝗶𝗻𝗸 👇:- 

https://pdlink.in/45LLDH7

🎓 Perfect for Students | Freshers | Data Analyst Aspirants | Beginners

If you aspire to work in top product companies, here’s my advice:

👉 For SDE-1 or SWE positions, focus on:

✔️ Continuously upskilling and improving your abilities.
✔️ Developing strong problem-solving skills.
✔️Mastering DSA – trust me, you’ll be tested on it, so aim to excel.

Also, learn how to design scalable systems and understand how to build solutions that can handle growth in users and data.

👉 For higher-level roles (SDE-2 and SDE-3), focus on:

✔️ DSA + System Design (both LLD and HLD).
✔️ Building your leadership skills, as you’ll need to lead teams and projects.

🔸I know it’s challenging to do this while working full-time, but you’ll need to carve out time to consistently upskill yourself.

Remember, your learning plan should be sensible and well-organized.

Best Programming Resources: https://topmate.io/coding/886839

ENJOY LEARNING 👍👍