Qualcomm is hiring Associate Engineer
For 2023, 2024, 2025 gards
Location: Hyderabad
https://careers.qualcomm.com/careers/job/446715365662-engineer-associate-hyderabad-telangana-india?domain=qualcomm.com
For 2023, 2024, 2025 gards
Location: Hyderabad
https://careers.qualcomm.com/careers/job/446715365662-engineer-associate-hyderabad-telangana-india?domain=qualcomm.com
Qualcomm
Engineer, Associate | Qualcomm Careers | Engineering Jobs and More | Qualcomm
Search open positions at Qualcomm. Learn more about how our culture of collaboration and robust benefits program allow our employees to live well and exceed their potential.
❤1
✅ Machine Learning Interview Questions & Answers 🎯
1. What is the difference between supervised and unsupervised learning
Answer:
Supervised learning uses labeled data to learn a mapping from inputs to outputs (e.g., predicting house prices). Unsupervised learning finds hidden patterns or groupings in unlabeled data (e.g., customer segmentation using K-Means).
2. How do you handle missing values during feature engineering
Answer:
Common strategies include:
– Imputation: Fill missing values with mean, median, or mode
– Deletion: Remove rows or columns with excessive missing data
– Model-based: Use predictive models to estimate missing values
3. What is the bias-variance tradeoff
Answer:
Bias refers to error due to overly simplistic assumptions; variance refers to error due to model sensitivity to small fluctuations in training data. A good model balances both to avoid underfitting (high bias) and overfitting (high variance).
4. Explain how Random Forest reduces overfitting
Answer:
Random Forest uses bagging (bootstrap aggregation) and builds multiple decision trees on random subsets of data and features. It averages their predictions, reducing variance and improving generalization.
5. What is the role of cross-validation in model selection
Answer:
Cross-validation (e.g., k-fold) splits data into multiple training/testing sets to evaluate model performance more reliably. It helps prevent overfitting and ensures the model generalizes well to unseen data.
6. How does XGBoost differ from traditional boosting methods
Answer:
XGBoost uses gradient boosting with regularization (L1 and L2), tree pruning, and parallel processing. It’s faster and more accurate than traditional boosting algorithms like AdaBoost.
7. What is the difference between L1 and L2 regularization
Answer:
– L1 (Lasso): Adds absolute value of weights to loss function, promoting sparsity
– L2 (Ridge): Adds squared value of weights, penalizing large weights and improving stability
8. How would you deploy a trained ML model
Answer:
– Serialize the model using pickle or joblib
– Create a REST API using Flask or FastAPI
– Monitor performance using metrics like latency, accuracy drift, and feedback loops
9. What is the difference between precision and recall
Answer:
– Precision: True Positives / (True Positives + False Positives)
– Recall: True Positives / (True Positives + False Negatives)
Precision focuses on correctness of positive predictions; recall focuses on capturing all actual positives.
10. What is the Q-value in reinforcement learning
Answer:
Q-value represents the expected cumulative reward of taking an action in a given state and following a policy thereafter. It’s central to Q-learning algorithms.
❤️ Tap for more
1. What is the difference between supervised and unsupervised learning
Answer:
Supervised learning uses labeled data to learn a mapping from inputs to outputs (e.g., predicting house prices). Unsupervised learning finds hidden patterns or groupings in unlabeled data (e.g., customer segmentation using K-Means).
2. How do you handle missing values during feature engineering
Answer:
Common strategies include:
– Imputation: Fill missing values with mean, median, or mode
– Deletion: Remove rows or columns with excessive missing data
– Model-based: Use predictive models to estimate missing values
3. What is the bias-variance tradeoff
Answer:
Bias refers to error due to overly simplistic assumptions; variance refers to error due to model sensitivity to small fluctuations in training data. A good model balances both to avoid underfitting (high bias) and overfitting (high variance).
4. Explain how Random Forest reduces overfitting
Answer:
Random Forest uses bagging (bootstrap aggregation) and builds multiple decision trees on random subsets of data and features. It averages their predictions, reducing variance and improving generalization.
5. What is the role of cross-validation in model selection
Answer:
Cross-validation (e.g., k-fold) splits data into multiple training/testing sets to evaluate model performance more reliably. It helps prevent overfitting and ensures the model generalizes well to unseen data.
6. How does XGBoost differ from traditional boosting methods
Answer:
XGBoost uses gradient boosting with regularization (L1 and L2), tree pruning, and parallel processing. It’s faster and more accurate than traditional boosting algorithms like AdaBoost.
7. What is the difference between L1 and L2 regularization
Answer:
– L1 (Lasso): Adds absolute value of weights to loss function, promoting sparsity
– L2 (Ridge): Adds squared value of weights, penalizing large weights and improving stability
8. How would you deploy a trained ML model
Answer:
– Serialize the model using pickle or joblib
– Create a REST API using Flask or FastAPI
– Monitor performance using metrics like latency, accuracy drift, and feedback loops
9. What is the difference between precision and recall
Answer:
– Precision: True Positives / (True Positives + False Positives)
– Recall: True Positives / (True Positives + False Negatives)
Precision focuses on correctness of positive predictions; recall focuses on capturing all actual positives.
10. What is the Q-value in reinforcement learning
Answer:
Q-value represents the expected cumulative reward of taking an action in a given state and following a policy thereafter. It’s central to Q-learning algorithms.
❤️ Tap for more
❤2
Capco
Position: Financial Accounting
Qualifications: Bachelor’s/ Master’s Degree
Experience: Freshers/ Experienced
Location: Across India
📌Apply Now: https://www.capco.com/en/Careers/Job%20Search/Office%20Detail?gh_jid=7427797&location=36f0686bfe4b44afa75f218f838e0fdc&department=&keywords=
Position: Financial Accounting
Qualifications: Bachelor’s/ Master’s Degree
Experience: Freshers/ Experienced
Location: Across India
📌Apply Now: https://www.capco.com/en/Careers/Job%20Search/Office%20Detail?gh_jid=7427797&location=36f0686bfe4b44afa75f218f838e0fdc&department=&keywords=
❤1
Complete DSA Roadmap
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| └─ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | └─ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | └ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | └ Bellman-Ford_Algorithm
| | |
| | └─ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | └ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | └─ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| └─ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| └─ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| └─ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| └─ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| └─ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| └─ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| └─ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| └─ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | └─ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | └─ Mobius_Function
| |
| └─ String_Algorithms
| |-- KMP_Algorithm
| └─ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
Tap ❤️ for more!
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| └─ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | └─ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | └ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | └ Bellman-Ford_Algorithm
| | |
| | └─ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | └ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | └─ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| └─ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| └─ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| └─ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| └─ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| └─ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| └─ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| └─ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| └─ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | └─ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | └─ Mobius_Function
| |
| └─ String_Algorithms
| |-- KMP_Algorithm
| └─ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
Tap ❤️ for more!
❤7👍1
🧠 Scenario-Based SQL Interview Question (Asked Often)
📌 Scenario:
You’re a Data Analyst at an e-commerce company.
There’s an orders table with these columns:
order_id, customer_id, order_date, order_amount
👉 Interview Question:
Find customers who placed more than 1 order on the same day, and show the total amount they spent on that day.
⏳ Take 10 seconds. How would you think?
✅ SQL Approach
1️⃣ Group data by customer_id and order_date
2️⃣ Count orders per day
3️⃣ Sum total order value
4️⃣ Filter customers with more than one order
💡 SQL Query
SELECT
customer_id,
order_date,
COUNT(order_id) AS total_orders,
SUM(order_amount) AS total_spent
FROM orders
GROUP BY customer_id, order_date
HAVING COUNT(order_id) > 1;
Drop a 🔥 if you want tougher SQL questions next
📌 Scenario:
You’re a Data Analyst at an e-commerce company.
There’s an orders table with these columns:
order_id, customer_id, order_date, order_amount
👉 Interview Question:
Find customers who placed more than 1 order on the same day, and show the total amount they spent on that day.
⏳ Take 10 seconds. How would you think?
✅ SQL Approach
1️⃣ Group data by customer_id and order_date
2️⃣ Count orders per day
3️⃣ Sum total order value
4️⃣ Filter customers with more than one order
💡 SQL Query
SELECT
customer_id,
order_date,
COUNT(order_id) AS total_orders,
SUM(order_amount) AS total_spent
FROM orders
GROUP BY customer_id, order_date
HAVING COUNT(order_id) > 1;
Drop a 🔥 if you want tougher SQL questions next
❤2🔥2
🐍 PYTHON TRICK #1
❌ Wrong way:
✅ Right way:
💡 5 lines → 1 line!
In the first (wrong/long) way, we are using a traditional loop. Python reads each number one by one, performs the square operation, and then manually adds the result into a list using append(). This works perfectly fine, but it’s longer, slower to write, and less “Pythonic.” When code grows bigger, these extra lines make programs harder to read.
Now look at the second (right) way — this is called a List Comprehension. It combines loop + expression + list creation into a single clean line:
👉 squares = [num**2 for num in [1,2,3,4,5]]
Python is designed to read almost like English. This line literally means:
“For every number in the list, square it, and store the result in a new list.”
🚀 Why List Comprehensions are powerful:
✔️ Shorter code
✔️ Easier to read once you practice
✔️ Faster execution in many cases
✔️ Used heavily in Data Science & AI
✔️ Makes you look like a pro Python developer
This is the difference between just writing code and writing smart Python code 💻🔥
Start using this habit in small problems, and soon it’ll become your natural style.
Share with Credit https://t.me/codingdidi
Tap ❤️ for more!
❌ Wrong way:
squares = [ ]
for num in [1,2,3,4,5]:
squares.append(num**2)
✅ Right way:
squares = [num**2 for num in [1,2,3,4,5]]
💡 5 lines → 1 line!
In the first (wrong/long) way, we are using a traditional loop. Python reads each number one by one, performs the square operation, and then manually adds the result into a list using append(). This works perfectly fine, but it’s longer, slower to write, and less “Pythonic.” When code grows bigger, these extra lines make programs harder to read.
Now look at the second (right) way — this is called a List Comprehension. It combines loop + expression + list creation into a single clean line:
👉 squares = [num**2 for num in [1,2,3,4,5]]
Python is designed to read almost like English. This line literally means:
“For every number in the list, square it, and store the result in a new list.”
🚀 Why List Comprehensions are powerful:
✔️ Shorter code
✔️ Easier to read once you practice
✔️ Faster execution in many cases
✔️ Used heavily in Data Science & AI
✔️ Makes you look like a pro Python developer
This is the difference between just writing code and writing smart Python code 💻🔥
Start using this habit in small problems, and soon it’ll become your natural style.
Share with Credit https://t.me/codingdidi
Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
❤1
💰 EARNING TIP #1
I recently shared about this on my instagram channel, So here's the process
Reddit se freelance kaam milta hai! Yahan start karo:
🎯 r/forhire
🎯 r/slavelabour
Process:
1️⃣ Account banao
2️⃣ Karma build karo (help people)
3️⃣ Browse daily
4️⃣ Reply fast
5️⃣ Get paid directly!
Per project: $20-100 (₹1,700-8,400)
Share with Credit https://t.me/codingdidi
Tap ❤️ for more!
I recently shared about this on my instagram channel, So here's the process
Reddit se freelance kaam milta hai! Yahan start karo:
🎯 r/forhire
🎯 r/slavelabour
Process:
1️⃣ Account banao
2️⃣ Karma build karo (help people)
3️⃣ Browse daily
4️⃣ Reply fast
5️⃣ Get paid directly!
Per project: $20-100 (₹1,700-8,400)
Share with Credit https://t.me/codingdidi
Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
❤2👍1
SQL Interview Trap 🚨 Consecutive Orders Logic
You have a table:
orders
order_id | customer_id | order_date | amount
👉 Question:
Find customers who placed orders on 3 or more consecutive days,
but return only the first date of each such streak per customer.
⚠️ No temp tables.
⚠️ Assume multiple orders per day are possible.
🧠 Most candidates fail because they:
- Forget to handle multiple orders on the same day
- Misuse ROW_NUMBER()
- Miss the date gap logic
✅ Correct SQL Solution:
WITH distinct_orders AS (
SELECT DISTINCT customer_id, order_date
FROM orders
),
grp AS (
SELECT
customer_id,
order_date,
order_date - INTERVAL '1 day' *
ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS grp_id
FROM distinct_orders
)
SELECT
customer_id,
MIN(order_date) AS streak_start_date
FROM grp
GROUP BY customer_id, grp_id
HAVING COUNT(*) >= 3;
💡 Why this works (Interview Gold):
- DISTINCT removes same-day duplicates
- ROW_NUMBER() creates a sequence
- Date minus row number groups consecutive dates
- HAVING COUNT(*) >= 3 filters valid streaks
🔥 React with 🔥 if this bent your brain
Share with Credit https://t.me/codingdidi
📌 Follow the channel for REAL interview-level SQL Content
You have a table:
orders
order_id | customer_id | order_date | amount
👉 Question:
Find customers who placed orders on 3 or more consecutive days,
but return only the first date of each such streak per customer.
⚠️ No temp tables.
⚠️ Assume multiple orders per day are possible.
🧠 Most candidates fail because they:
- Forget to handle multiple orders on the same day
- Misuse ROW_NUMBER()
- Miss the date gap logic
✅ Correct SQL Solution:
WITH distinct_orders AS (
SELECT DISTINCT customer_id, order_date
FROM orders
),
grp AS (
SELECT
customer_id,
order_date,
order_date - INTERVAL '1 day' *
ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS grp_id
FROM distinct_orders
)
SELECT
customer_id,
MIN(order_date) AS streak_start_date
FROM grp
GROUP BY customer_id, grp_id
HAVING COUNT(*) >= 3;
💡 Why this works (Interview Gold):
- DISTINCT removes same-day duplicates
- ROW_NUMBER() creates a sequence
- Date minus row number groups consecutive dates
- HAVING COUNT(*) >= 3 filters valid streaks
🔥 React with 🔥 if this bent your brain
Share with Credit https://t.me/codingdidi
📌 Follow the channel for REAL interview-level SQL Content
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
🔥3❤1
✅ Essential Tools for Data Analytics 📊🛠
🔣 1️⃣ Excel / Google Sheets
• Quick data entry & analysis
• Pivot tables, charts, functions
• Good for early-stage exploration
💻 2️⃣ SQL (Structured Query Language)
• Work with databases (MySQL, PostgreSQL, etc.)
• Query, filter, join, and aggregate data
• Must-know for data from large systems
🐍 3️⃣ Python (with Libraries)
• Pandas – Data manipulation
• NumPy – Numerical analysis
• Matplotlib / Seaborn – Data visualization
• OpenPyXL / xlrd – Work with Excel files
📊 4️⃣ Power BI / Tableau
• Create dashboards and visual reports
• Drag-and-drop interface for non-coders
• Ideal for business insights & presentations
📁 5️⃣ Google Data Studio
• Free dashboard tool
• Connects easily to Google Sheets, BigQuery
• Great for real-time reporting
🧪 6️⃣ Jupyter Notebook
• Interactive Python coding
• Combine code, text, and visuals in one place
• Perfect for storytelling with data
🛠 7️⃣ R Programming (Optional)
• Popular in statistical analysis
• Strong in academic and research settings
☁️ 8️⃣ Cloud & Big Data Tools
• Google BigQuery, Snowflake – Large-scale analysis
• Excel + SQL + Python still work as a base
💡 Tip:
Start with Excel + SQL + Python (Pandas) → Add BI tools for reporting.
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
🔣 1️⃣ Excel / Google Sheets
• Quick data entry & analysis
• Pivot tables, charts, functions
• Good for early-stage exploration
💻 2️⃣ SQL (Structured Query Language)
• Work with databases (MySQL, PostgreSQL, etc.)
• Query, filter, join, and aggregate data
• Must-know for data from large systems
🐍 3️⃣ Python (with Libraries)
• Pandas – Data manipulation
• NumPy – Numerical analysis
• Matplotlib / Seaborn – Data visualization
• OpenPyXL / xlrd – Work with Excel files
📊 4️⃣ Power BI / Tableau
• Create dashboards and visual reports
• Drag-and-drop interface for non-coders
• Ideal for business insights & presentations
📁 5️⃣ Google Data Studio
• Free dashboard tool
• Connects easily to Google Sheets, BigQuery
• Great for real-time reporting
🧪 6️⃣ Jupyter Notebook
• Interactive Python coding
• Combine code, text, and visuals in one place
• Perfect for storytelling with data
🛠 7️⃣ R Programming (Optional)
• Popular in statistical analysis
• Strong in academic and research settings
☁️ 8️⃣ Cloud & Big Data Tools
• Google BigQuery, Snowflake – Large-scale analysis
• Excel + SQL + Python still work as a base
💡 Tip:
Start with Excel + SQL + Python (Pandas) → Add BI tools for reporting.
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
❤1
📚 FREE RESOURCE ALERT!
Best FREE Python courses for beginners:
1️⃣ FreeCodeCamp YouTube - 4.5 hrs complete course
2️⃣ Python.org official tutorial
3️⃣ Kaggle Learn Python - hands-on
4️⃣ Codecademy Python basics
Mera recommendation: Start with FreeCodeCamp!
Save kar lo! 🔖
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Best FREE Python courses for beginners:
1️⃣ FreeCodeCamp YouTube - 4.5 hrs complete course
2️⃣ Python.org official tutorial
3️⃣ Kaggle Learn Python - hands-on
4️⃣ Codecademy Python basics
Mera recommendation: Start with FreeCodeCamp!
Save kar lo! 🔖
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
🎯 MINI CHALLENGE #1
Can you solve this in Python?
Print numbers 1-100:
• "Fizz" if divisible by 3
• "Buzz" if divisible by 5
• "FizzBuzz" if divisible by both
• Number otherwise
Example:
1, 2, Fizz, 4, Buzz, Fizz, 7...
⏰ Time limit: 10 minutes
Let's see who all can solve!!
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Can you solve this in Python?
Print numbers 1-100:
• "Fizz" if divisible by 3
• "Buzz" if divisible by 5
• "FizzBuzz" if divisible by both
• Number otherwise
Example:
1, 2, Fizz, 4, Buzz, Fizz, 7...
⏰ Time limit: 10 minutes
Let's see who all can solve!!
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
🚀 WEEKEND PROJECT
Banao apna Password Generator!
Features:
✅ Random password (8-16 chars)
✅ Include numbers, symbols
✅ Copy to clipboard
✅ Save to file
Libraries needed:
• random
• string
• pyperclip
⏰ Time: 2-3 hours
Interested? React with 🔥 and I'll share full tutorial!
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Banao apna Password Generator!
Features:
✅ Random password (8-16 chars)
✅ Include numbers, symbols
✅ Copy to clipboard
✅ Save to file
Libraries needed:
• random
• string
• pyperclip
⏰ Time: 2-3 hours
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📊 WEEK 1 COMPLETE! 🎉
Is hafte humne seekha:
✅ List comprehension
✅ Reddit freelancing
✅ Free resources
✅ FizzBuzz challenge
✅ Password generator idea
📌 POLL: Next week kya seekhna hai?
A) Web scraping basics
B) Excel automation
C) Discord bot tutorial
D) Data analysis
Reply with A/B/C/D! 👇
Share with Credit https://t.me/codingdidi
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Is hafte humne seekha:
✅ List comprehension
✅ Reddit freelancing
✅ Free resources
✅ FizzBuzz challenge
✅ Password generator idea
📌 POLL: Next week kya seekhna hai?
A) Web scraping basics
B) Excel automation
C) Discord bot tutorial
D) Data analysis
Reply with A/B/C/D! 👇
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
❤2
🐍 PYTHON TRICK #2
❌ Old way (2015):
name = "Amit"
age = 25
print("Name: {}, Age: {}".format(name, age))
✅ Modern way (2024):
print(f"Name: {name}, Age: {age}")
💡 F-strings are:
• Faster
• Cleaner
• More readable
Bonus:
print(f"Result: {5 + 3}") # 8
print(f"Name: {name.upper()}") # AMIT
#Python #FStrings
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❌ Old way (2015):
name = "Amit"
age = 25
print("Name: {}, Age: {}".format(name, age))
✅ Modern way (2024):
print(f"Name: {name}, Age: {age}")
💡 F-strings are:
• Faster
• Cleaner
• More readable
Bonus:
print(f"Result: {5 + 3}") # 8
print(f"Name: {name.upper()}") # AMIT
#Python #FStrings
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@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
💰 EARNING TIP #2
Telegram groups se kaam milta hai!
Join these groups:
📌 "Freelance Jobs India"
📌 "Python Developers India"
📌 "Remote Jobs India"
📌 "Startup Jobs"
Tips:
✅ Active raho daily
✅ DM directly (don't spam)
✅ Share portfolio
✅ Build trust
Maine personally ₹8K ka project liya tha!
Note: For telegram you guys needs to be really active, remember to not to pay.
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram groups se kaam milta hai!
Join these groups:
📌 "Freelance Jobs India"
📌 "Python Developers India"
📌 "Remote Jobs India"
📌 "Startup Jobs"
Tips:
✅ Active raho daily
✅ DM directly (don't spam)
✅ Share portfolio
✅ Build trust
Maine personally ₹8K ka project liya tha!
Note: For telegram you guys needs to be really active, remember to not to pay.
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
🎯 MUST-HAVE VS CODE EXTENSIONS
1️⃣ Python (Microsoft) - Auto-complete
2️⃣ Pylance - Fast IntelliSense
3️⃣ autoDocstring - Auto documentation
4️⃣ Better Comments - Colorful comments
5️⃣ Error Lens - Inline errors
6️⃣ Material Icon Theme - Beautiful icons
Install karo aur coding 10x better! 💪
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
1️⃣ Python (Microsoft) - Auto-complete
2️⃣ Pylance - Fast IntelliSense
3️⃣ autoDocstring - Auto documentation
4️⃣ Better Comments - Colorful comments
5️⃣ Error Lens - Inline errors
6️⃣ Material Icon Theme - Beautiful icons
Install karo aur coding 10x better! 💪
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
❤2
📚 WEB SCRAPING 101
Simple example - Scrape website titles:
Libraries needed:
• requests
• beautifulsoup4
Install: pip install requests beautifulsoup4
Full tutorial chahiye? 👀
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Simple example - Scrape website titles:
import requests
from bs4 import BeautifulSoup
url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.find('h1').text
print(title)
Libraries needed:
• requests
• beautifulsoup4
Install: pip install requests beautifulsoup4
Full tutorial chahiye? 👀
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Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
💡 REAL STORY
6 months ago:
❌ No Python knowledge
❌ No freelancing experience
❌ ₹0 earning
Today:
✅ 20+ projects completed
✅ ₹45,000+ total earned
✅ 3 regular clients
Secret? CONSISTENCY.
Daily:
• 2 hours learning
• 1 hour applying for jobs
• 3 hours working
Tum bhi kar sakte ho! 💪
Kisne start kiya? 🙋
#Motivation #Success #CodingDidi
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6 months ago:
❌ No Python knowledge
❌ No freelancing experience
❌ ₹0 earning
Today:
✅ 20+ projects completed
✅ ₹45,000+ total earned
✅ 3 regular clients
Secret? CONSISTENCY.
Daily:
• 2 hours learning
• 1 hour applying for jobs
• 3 hours working
Tum bhi kar sakte ho! 💪
Kisne start kiya? 🙋
#Motivation #Success #CodingDidi
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Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
The Smartest Career Upgrade in 2026: Google AI Professional Certificate
The Google AI Professional Certificate equips working professionals with practical skills to integrate AI into their daily work through a modular, hands-on design.
Tap ❤️ for more!
The Google AI Professional Certificate equips working professionals with practical skills to integrate AI into their daily work through a modular, hands-on design.
Content: 6 short courses and a capstone project.
Tools Used: Gemini, NotebookLM, and AI Studio.
Outcome: Learners build custom AI-powered solutions and earn a certificate trusted by leading employers.
Exclusive Benefit: Learners receive three months of no-cost access to Google AI Pro to practice within tools like Gmail and Google Docs.
Master AI by Doing: Over 20 hands-on labs solving real-world problems.
Job-Ready Skills: Built using real-world job data to teach exactly what employers want right now.
Future-Proofing: Helps professionals stay ahead in a fast-changing environment by learning directly from Google experts.
Beyond Prompting: Learn to build actual workflows and custom apps to solve workplace challenges
link to enroll: https://imp.i384100.net/JkbeNa Tap ❤️ for more!
Coursera
Google AI
Offered by Google. Build your AI fluency and get more ... Enroll for free.
🎯 MINI CHALLENGE #2
Write a function to check if a word is palindrome!
Example:
• "racecar" → True
• "hello" → False
• "madam" → True
Bonus: Ignore spaces & case
"A man a plan a canal Panama" → True
⏰ Time: 15 minutes
Try krke Done Reply karo
Solution tomorrow! 👀
#CodingChallenge #Python
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Write a function to check if a word is palindrome!
Example:
• "racecar" → True
• "hello" → False
• "madam" → True
Bonus: Ignore spaces & case
"A man a plan a canal Panama" → True
⏰ Time: 15 minutes
Try krke Done Reply karo
Solution tomorrow! 👀
#CodingChallenge #Python
Share with Credit https://t.me/codingdidi
💬 Tap ❤️ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates