Coursera interview experience
Role - ML engineer

It started with his introduction and then mine. Next he gave me one hackerrank link to solve live a medium to hard level leetcode question based on arrays. After completing that, he asked me few ML based questions like type of learning and their explanations and a few examples. Also asked me to classify 2 examples about what ML model might be suitable for that case. Also he asked me a few SQL queries. Then he asked me to choose a project which i did and asked me to explain it and cross questioned about that. At last he gave me a chance to ask a few questions.


🎉For Placement Resources:
https://topmate.io/sumit_kumar80/1261910?utm_source=public_profile&utm_campaign=sumit_kumar80

Rtcamp interview experience
Role - Associate Python Engineer

It started with a general room with around 50 candidates. After a short introduction we were moved to a personal breakout room.
Then the interviewer asked about the introduction and a few questions based on dbms, hashing, open source, web development
Questions included -
- what is indexing
- how will you store a password, indexing or hashing, why?
- python vs javascript
- frameworks vs libraries
- git vs github
- session vs cookie
- can you hash an image
- can hashing be reversed
- how is the hashed image stored and retrieved
- sql commands and complexity

🎉Placement Resources:
https://topmate.io/sumit_kumar80

📌PharmEasy is hiring for Software Engineer Intern (Frontend)
Experience: 0 - 2 year's
Expected Stipend: 4-6 LPA
Apply here: https://www.linkedin.com/jobs/view/4309726517/

📌Lam Research is hiring for Engineering Intern
Experience: 0 - 2 year's
Apply here: https://careers.lamresearch.com/careers/job/1099542488778?hl=en_US&domain=lamresearch.com&src=Eightfold

📌Accenture is hiring for Software Development Engineer
Experience: 0 - 2 year's
Apply here: https://www.accenture.com/in-en/careers/jobdetails?id=ATCI-5115212-S1892199_en&title=Software+Development+Engineer

📌Mphasis is hiring for Junior Trainee Role
Experience: 0 - 2 year's
Apply here: https://mphasis.ripplehire.com/candidate/?token=B4UkLILwjQRHGDZ8Zziq&source=LINKEDINCAMPAIGNNEW&ref=LINKEDINCAMPAIGNNEW#detail/job/654040

🔹 What is the Bandwagon Effect?

The Bandwagon Effect is a psychological bias in which people start doing something simply because everyone else is doing it.

👉 That is, a person doesn't think whether something is right or not, they just follow the crowd.
This is called —

"Everyone is doing it, so I will too."

🔹 Real-life examples 👇

Fashion trends 👗
When a new style or clothing item becomes trendy, people start wearing it,
even if it doesn't suit them — just because everyone else is wearing it.

Social media following 📱
When a celebrity or influencer becomes very popular,
people start following them without any particular reason,
because “everyone is following them.”

Voting in elections 🗳️
Sometimes people vote for a party just because
“it seems like they are going to win.”
That is, they make decisions based on the majority, not thoughtful consideration.

Stock market / Crypto 💰
When people see others investing in a particular stock or cryptocurrency,
they also invest in it —
without knowing whether it's the right choice or not.

🔹 Psychological reason

Humans feel safe in a crowd (herd mentality).

We fear that “if I am different, I might be wrong.”

That's why we do what others are doing.

🔹 Example from India 🇮🇳

In 2021, when cryptocurrencies like “Dogecoin” and “Shiba Inu” were trending,
millions of Indians invested money just because
“everyone was earning.”
But later, when the market crashed,
most people suffered heavy losses —
because they hadn't done their own research.

✅ Simple definition:

Bandwagon Effect means – when a person starts doing something just because everyone else is doing it.

🕉 Even in matters of religion, people follow new rituals. They don't even know their meaning, yet they follow them. Why? Because if everyone is doing it, it can't be wrong.

But I have learned from Acharya Ji that if everyone is doing something, there's a higher chance that it might be wrong. And I am seeing this happening around me.

https://www.linkedin.com/company/metanotion/

Follow this page for any job updates

Do you remember the lockdown days?

When the world came to a halt, flights grounded, factories silent, roads empty.

And suddenly, skies turned blue.

Rivers that hadn’t reflected the sun in decades began to sparkle.

Peacocks danced on highways, dolphins appeared near city shores, and mountains became visible from hundreds of kilometres away.

All within weeks.

That wasn’t a miracle.

It was proof, nature heals fast when we step aside.

And that’s what the Earth needs today, not more symbolic action, but less action.

Not more planting, but less cutting.

Not more green slogans, but fewer desires that demand destruction.

————————————

As Acharya Prashant reminds us,

“The solution is not to add new eco-friendly gestures on top of the same destructive lifestyle, but to stop that lifestyle itself.”

Because the more we “do”, the more we disturb.

We pollute when we act, and we pollute again when we try to undo that act.

It’s All Analytics!
The foundations of AI, big data, and data science landscape for professionals in healthcare,.business, and government
Scott Burk & Gary D. Miner
CRC Press, 2021

It’s All Analytics!
Scott Burk, 2021

30-day roadmap to learn Java up to an intermediate level.

This roadmap is designed for beginners, so adjust your pace as needed.

Week 1: Java Basics
*Day 1-2:*
- Day 1: Get Java installed on your computer and set up your development environment.
- Day 2: Learn about Java's history, its role in programming, and write your first "Hello, World!" program.

*Day 3-4:*
- Day 3: Study Java syntax, data types, and variables.
- Day 4: Understand operators and perform basic arithmetic operations.

*Day 5-7:*
- Day 5: Explore control flow with if-else statements and loops (for and while).
- Day 6: Dive into switch statements and understand how to handle user choices.
- Day 7: Practice writing small programs that use conditions and loops.

Week 2: Functions and Object-Oriented Programming
*Day 8-9:*
- Day 8: Learn about functions (methods) and how to define your own functions in Java.
- Day 9: Study function parameters, return types, and method overloading.

*Day 10-12:*
- Day 10: Understand the basics of object-oriented programming (OOP) in Java.
- Day 11: Learn about classes, objects, and constructors.
- Day 12: Explore encapsulation, inheritance, and polymorphism.

*Day 13-14:*
- Day 13: Study Java packages and access modifiers (public, private, protected).
- Day 14: Practice creating classes and objects in real-world scenarios.

Week 3: Data Structures and Collections
*Day 15-17:*
- Day 15: Dive into arrays in Java and understand their usage.
- Day 16: Study Java's collection framework and ArrayList.
- Day 17: Learn about iterating through collections using loops and iterators.

*Day 18-19:*
- Day 18: Explore other collection types like LinkedList and HashMap.
- Day 19: Understand when to use different collection types in Java.

*Day 20-21:*
- Day 20: Study exception handling in Java and how to deal with errors.
- Day 21: Practice working with try-catch blocks and handling exceptions effectively.

Week 4: Intermediate Topics and Projects
*Day 22-23:*
- Day 22: Study file handling in Java, including reading and writing files.
- Day 23: Create a small project that involves file operations.

*Day 24-26:*
- Day 24: Learn about multithreading and how to create and manage threads in Java.
- Day 25: Study Java's built-in libraries for networking and socket programming.
- Day 26: Work on a project that involves multithreading or networking.

*Day 27-28:*
- Day 27: Explore more advanced Java topics like JavaFX for GUI development or JDBC for database connectivity.
- Day 28: Work on a more complex project that combines your knowledge from the past weeks.

*Day 29-30:*
- Day 29: Review and revisit any topics you found challenging.
- Day 30: Continue building projects and exploring areas of Java that interest you.

Consider joining Java communities and forums to seek help and advice. Java is a versatile language with many applications, so your learning journey can continue well beyond this roadmap. Good luck!

Javascript  for Everything:

JS + React = Web Development
JS + Three.js = 3D Visualization
JS + Angular = Web Applications

JS + Phaser = Game Development
JS + Vue.js = Progressive Web Apps
JS + TensorFlow.js = Machine Learning

JS + Node.js = Server-Side Development
JS + Electron = DesktopApp Development
JS + React Native = MobileApp Development

#javascript

Top Coding Interview Questions 💻

📍 1. Two Sum Problem
Find two numbers in an array that add up to a target value.
Approach: Use a hash map to store complements for O(n) time.

📍 2. Reverse a Linked List
Reverse a singly linked list iteratively or recursively.

📍 3. Binary Tree Traversals
Implement Inorder, Preorder, and Postorder traversals (recursion or stack).

📍 4. Detect Cycle in a Linked List
Use Floyd’s Tortoise and Hare algorithm to detect if a loop exists.

📍 5. Merge Intervals
Given intervals, merge all overlapping intervals.

📍 6. Valid Parentheses
Use a stack to check for matching pairs of parentheses/brackets.

📍 7. Maximum Subarray Sum (Kadane’s Algorithm)
Find the contiguous subarray with the largest sum.

📍 8. Search in a Rotated Sorted Array
Modified binary search to find an element in a rotated sorted array.

📍 9. Implement Queue using Stacks
Use two stacks to simulate a queue’s FIFO behavior.

📍 🔟 Least Recently Used (LRU) Cache Implementation
Use a hashmap + doubly linked list for O(1) access and updates.

💡 Pro Tip: Master these core problems and practice explaining your thought process clearly. Also, get comfortable with coding on whiteboard or online editors.

Algorithms and Data Structures with Python
An interactive learning experience
4 book projects
Cuantum Technologies, 2023

Algorithms and Data Structures with Python
Cuantum Technologies, 2023

✅ Top Deep Learning Interview Questions & Answers 🤖🧠

📍 1. What is Deep Learning?
Answer: A subset of Machine Learning that uses multi-layered neural networks to learn patterns from large datasets. It excels in image recognition, speech processing, and NLP.

📍 2. What is a Neural Network?
Answer: A system of interconnected nodes (neurons) organized in layers — input, hidden, and output — that process data using weights and activation functions.

📍 3. What are Activation Functions?
Answer: They introduce non-linearity into the network. Common types:
⦁ ReLU: max(0, x) — fast and widely used
⦁ Sigmoid: outputs between 0 and 1
⦁ Tanh: outputs between -1 and 1

📍 4. What is Backpropagation?
Answer: The process of updating weights in a neural network by calculating the gradient of the loss function and propagating it backward using chain rule.

📍 5. What is Dropout?
Answer: A regularization technique that randomly disables neurons during training to prevent overfitting.

📍 6. What is Transfer Learning?
Answer: Using a pre-trained model on a new, related task. Example: fine-tuning ResNet for medical image classification.

📍 7. What are CNNs used for?
Answer: Convolutional Neural Networks are ideal for image and video data. They use filters to detect spatial hierarchies like edges, shapes, and textures.

📍 8. What are RNNs and LSTMs?
Answer:
⦁ RNNs handle sequential data but suffer from vanishing gradients.
⦁ LSTMs solve this using memory cells and gates to retain long-term dependencies.

📍 9. What are Autoencoders?
Answer: Unsupervised neural networks that compress data into a lower-dimensional form and then reconstruct it. Used in anomaly detection and denoising.

📍 10. What are GANs?
Answer: Generative Adversarial Networks consist of a Generator (creates fake data) and a Discriminator (detects fakes). Used in image synthesis, deepfakes, and art generation.

📍 11. What is Regularization in Deep Learning?
Answer: Techniques like L1/L2 penalties, Dropout, and Early Stopping help reduce overfitting by constraining model complexity.

📍 12. What is the Vanishing Gradient Problem?
Answer: In deep networks, gradients can become too small during backpropagation, making it hard to update weights. Solutions include using ReLU and batch normalization.

📍 13. What is Batch Normalization?
Answer: It normalizes inputs to each layer, stabilizing learning and speeding up training.

📍 14. What is the role of Epochs, Batches, and Iterations?
Answer:
⦁ Epoch: One full pass through the dataset
⦁ Batch: Subset of data used in one forward/backward pass
⦁ Iteration: One update of weights per batch

📍 15. What is the difference between Training and Inference?
Answer:
⦁ Training: Model learns from data
⦁ Inference: Model makes predictions using learned weights

💡 Pro Tip: Always explain concepts with examples or analogies in interviews. For instance, compare CNN filters to human vision detecting edges and shapes.

❤️ Tap for more AI/ML interview prep!

IQVIA hiring for Python Developer!!

📍Location: India (Hybrid)
Expected CTC: ₹60K – ₹1L per month
Qualification: Bachelor’s/ Master’s Degree
Experience: Freshers/ Experienced

Full Details & Apply Link:
https://jobs.iqvia.com/en/job/bengaluru/python-developer/24443/87326209296
[21/10, 8:08 pm] : Vodafone hiring for Executive – Junior Engineer!!

📍Location: Pune, India
Expected CTC: ₹30K – ₹70K per month
Qualification: Bachelor’s/ Master’s Degree
Experience: Freshers/ Experienced

Full Details & Apply Link:
https://jobs.vodafone.com/careers/job/563018693455424
[21/10, 8:08 pm] : eClerx hiring for Analyst!!

📍Location: Pune (Hybrid)
Expected CTC: ₹16K – ₹56K per month
Qualification: Graduate / Post Graduate

Full Details & Apply Link:
https://fa-ewji-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/80028

Company Name: Rubrik
Batch: 2026 passouts
Role: Software Internship - Winter 6 months
Stipend : 1.5L/month

Link : https://www.rubrik.com/company/careers/departments/job.7208329?gh_jid=7208329&gh_src=2e352a8a1us

Top Coding Interview Questions 💻

📍 1. Two Sum Problem
Find two numbers in an array that add up to a target value.
Approach: Use a hash map to store complements for O(n) time.

📍 2. Reverse a Linked List
Reverse a singly linked list iteratively or recursively.

📍 3. Binary Tree Traversals
Implement Inorder, Preorder, and Postorder traversals (recursion or stack).

📍 4. Detect Cycle in a Linked List
Use Floyd’s Tortoise and Hare algorithm to detect if a loop exists.

📍 5. Merge Intervals
Given intervals, merge all overlapping intervals.

📍 6. Valid Parentheses
Use a stack to check for matching pairs of parentheses/brackets.

📍 7. Maximum Subarray Sum (Kadane’s Algorithm)
Find the contiguous subarray with the largest sum.

📍 8. Search in a Rotated Sorted Array
Modified binary search to find an element in a rotated sorted array.

📍 9. Implement Queue using Stacks
Use two stacks to simulate a queue’s FIFO behavior.

📍 🔟 Least Recently Used (LRU) Cache Implementation
Use a hashmap + doubly linked list for O(1) access and updates.

💡 Pro Tip: Master these core problems and practice explaining your thought process clearly. Also, get comfortable with coding on whiteboard or online editors.

For More Resources Check out

https://topmate.io/sumit_kumar80/