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The Google AI Professional Certificate equips working professionals with practical skills to integrate AI into their daily work through a modular, hands-on design.
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π― MINI CHALLENGE #2
Write a function to check if a word is palindrome!
Example:
β’ "racecar" β True
β’ "hello" β False
β’ "madam" β True
Bonus: Ignore spaces & case
"A man a plan a canal Panama" β True
β° Time: 15 minutes
Try krke Done Reply karo
Solution tomorrow! π
#CodingChallenge #Python
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π¬ Tap β€οΈ for more!
Write a function to check if a word is palindrome!
Example:
β’ "racecar" β True
β’ "hello" β False
β’ "madam" β True
Bonus: Ignore spaces & case
"A man a plan a canal Panama" β True
β° Time: 15 minutes
Try krke Done Reply karo
Solution tomorrow! π
#CodingChallenge #Python
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π¬ Tap β€οΈ for more!
Telegram
@Codingdidi
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π WEEK 2 COMPLETE! π₯
This week, we covered:
β F-strings seekhe
β Telegram freelancing tips
β VS Code extensions
β Web scraping intro
β Palindrome challenge
Share with Credit https://t.me/codingdidi
π¬ Tap β€οΈ for more!
This week, we covered:
β F-strings seekhe
β Telegram freelancing tips
β VS Code extensions
β Web scraping intro
β Palindrome challenge
Share with Credit https://t.me/codingdidi
π¬ Tap β€οΈ for more!
Telegram
@Codingdidi
Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
These are top 5 data structures and algorithms projects, allowing you to dive deep into the world of DSA πͺπ»
β’Project 1: Snakes Game (Arrays)
The Snakes Game project is a classic implementation of the popular game
Snake.
This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.
β’Project 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)
The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts
β’Project 3: Sudoku Solver (Backtracking)
The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.
β’Project 4: File Zipper (Greedy Huffman
Encoder)
The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.
β’Project 5: Map Navigator (Dijkstraβs
Algorithm)
The Map Navigator project aims to develop a navigation system using Dijkstraβs algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.
Share with Credit https://t.me/codingdidi
Tap β€οΈ for more!
β’Project 1: Snakes Game (Arrays)
The Snakes Game project is a classic implementation of the popular game
Snake.
This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.
β’Project 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)
The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts
β’Project 3: Sudoku Solver (Backtracking)
The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.
β’Project 4: File Zipper (Greedy Huffman
Encoder)
The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.
β’Project 5: Map Navigator (Dijkstraβs
Algorithm)
The Map Navigator project aims to develop a navigation system using Dijkstraβs algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.
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β€1
π SQL Subqueries CTEs
1οΈβ£ What is a Subquery?
A subquery is a query inside another query. It runs first and passes its result to the outer query.
Think like this π
> βFirst find something β then use it to filter or calculate something elseβ
Why Subqueries exist (business thinking)
Real questions like:
β’ Find customers who spent more than average
β’ Find products with highest sales
β’ Find employees earning more than their manager
These need one queryβs result inside another query.
β Basic Subquery Structure
SELECT column
FROM text
WHERE column OPERATOR (
SELECT column
FROM text
);
Example Tables: orders
order_id | customer_id | amount
1 | 101 | 5000
2 | 102 | 8000
3 | 103 | 3000
2οΈβ£ Subquery in WHERE clause (Most Common)
πΉ Scenario: Find orders with amount greater than average order value
SELECT *
FROM orders
WHERE amount > (
SELECT AVG(amount)
FROM orders
);
What this query does
1. Inner query calculates average order amount
2. Outer query keeps only orders above that average
β Very common interview question
3οΈβ£ Subquery with IN
πΉ Scenario: Find customers who have placed at least one order
Tables: customers(customer_id, name) orders(customer_id)
SELECT name
FROM customers
WHERE customer_id IN (
SELECT customer_id
FROM orders
);
What this query does
β’ Inner query gets customers who ordered
β’ Outer query fetches their names
4οΈβ£ Subquery in SELECT clause
πΉ Scenario: Show each order with total number of orders
SELECT order_id, amount, (
SELECT COUNT(*)
FROM orders
) AS total_orders
FROM orders;
What this query does
β’ Inner query runs once
β’ Adds total order count to every row
β οΈ Use carefully β can be inefficient
5οΈβ£ Correlated Subquery (Important)
A correlated subquery depends on the outer query. It runs once per row.
πΉ Scenario: Find customers who spent more than their cityβs average
Tables: customers(customer_id, city) orders(customer_id, amount)
SELECT c.customer_id
FROM customers c
WHERE (
SELECT AVG(o.amount)
FROM orders o
WHERE o.customer_id = c.customer_id
) > 5000;
What this query does
β’ For each customer
β’ Calculates their average spend
β’ Filters based on condition
β οΈ Powerful but slower on large data
6οΈβ£ Problems with Subqueries
β Hard to read
β Hard to debug
β Performance issues
β Nested logic becomes messy
π Thatβs why CTEs exist
7οΈβ£ What is a CTE (Common Table Expression)?
A CTE is a named temporary result.
It makes complex queries readable and reusable.
CTE Syntax
WITH cte_name AS (
SELECT ...
)
SELECT *
FROM cte_name;
8οΈβ£ Same Problem Solved Using CTE (Cleaner)
πΉ Find customers with total spend > 10,000
WITH customer_spend AS (
SELECT customer_id, SUM(amount) AS total_spend
FROM orders
GROUP BY customer_id
)
SELECT *
FROM customer_spend
WHERE total_spend > 10000;
What this does
β’ First block calculates spend
β’ Second block filters results
β’ Very readable
9οΈβ£ CTE vs Subquery
β’ Readability: CTE is excellent, Subquery is poor
β’ Reusability: CTE is yes, Subquery is no
β’ Debugging: CTE is easy, Subquery is hard
β’ Performance: Both depend on usage
π When to Use What?
Use Subquery when:
βοΈ Logic is small
βοΈ Used only once
Use CTE when:
βοΈ Logic is complex
βοΈ Multiple steps
βοΈ Interview or production query
Common Beginner Mistakes
β Writing very deep nested subqueries
β Using correlated subqueries unnecessarily
β Forgetting CTE scope (only valid for one query)
Interview Tip π‘
> Subqueries solve problems inside queries, while CTEs solve readability and maintainability.
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1οΈβ£ What is a Subquery?
A subquery is a query inside another query. It runs first and passes its result to the outer query.
Think like this π
> βFirst find something β then use it to filter or calculate something elseβ
Why Subqueries exist (business thinking)
Real questions like:
β’ Find customers who spent more than average
β’ Find products with highest sales
β’ Find employees earning more than their manager
These need one queryβs result inside another query.
β Basic Subquery Structure
SELECT column
FROM text
WHERE column OPERATOR (
SELECT column
FROM text
);
Example Tables: orders
order_id | customer_id | amount
1 | 101 | 5000
2 | 102 | 8000
3 | 103 | 3000
2οΈβ£ Subquery in WHERE clause (Most Common)
πΉ Scenario: Find orders with amount greater than average order value
SELECT *
FROM orders
WHERE amount > (
SELECT AVG(amount)
FROM orders
);
What this query does
1. Inner query calculates average order amount
2. Outer query keeps only orders above that average
β Very common interview question
3οΈβ£ Subquery with IN
πΉ Scenario: Find customers who have placed at least one order
Tables: customers(customer_id, name) orders(customer_id)
SELECT name
FROM customers
WHERE customer_id IN (
SELECT customer_id
FROM orders
);
What this query does
β’ Inner query gets customers who ordered
β’ Outer query fetches their names
4οΈβ£ Subquery in SELECT clause
πΉ Scenario: Show each order with total number of orders
SELECT order_id, amount, (
SELECT COUNT(*)
FROM orders
) AS total_orders
FROM orders;
What this query does
β’ Inner query runs once
β’ Adds total order count to every row
β οΈ Use carefully β can be inefficient
5οΈβ£ Correlated Subquery (Important)
A correlated subquery depends on the outer query. It runs once per row.
πΉ Scenario: Find customers who spent more than their cityβs average
Tables: customers(customer_id, city) orders(customer_id, amount)
SELECT c.customer_id
FROM customers c
WHERE (
SELECT AVG(o.amount)
FROM orders o
WHERE o.customer_id = c.customer_id
) > 5000;
What this query does
β’ For each customer
β’ Calculates their average spend
β’ Filters based on condition
β οΈ Powerful but slower on large data
6οΈβ£ Problems with Subqueries
β Hard to read
β Hard to debug
β Performance issues
β Nested logic becomes messy
π Thatβs why CTEs exist
7οΈβ£ What is a CTE (Common Table Expression)?
A CTE is a named temporary result.
It makes complex queries readable and reusable.
CTE Syntax
WITH cte_name AS (
SELECT ...
)
SELECT *
FROM cte_name;
8οΈβ£ Same Problem Solved Using CTE (Cleaner)
πΉ Find customers with total spend > 10,000
WITH customer_spend AS (
SELECT customer_id, SUM(amount) AS total_spend
FROM orders
GROUP BY customer_id
)
SELECT *
FROM customer_spend
WHERE total_spend > 10000;
What this does
β’ First block calculates spend
β’ Second block filters results
β’ Very readable
9οΈβ£ CTE vs Subquery
β’ Readability: CTE is excellent, Subquery is poor
β’ Reusability: CTE is yes, Subquery is no
β’ Debugging: CTE is easy, Subquery is hard
β’ Performance: Both depend on usage
π When to Use What?
Use Subquery when:
βοΈ Logic is small
βοΈ Used only once
Use CTE when:
βοΈ Logic is complex
βοΈ Multiple steps
βοΈ Interview or production query
Common Beginner Mistakes
β Writing very deep nested subqueries
β Using correlated subqueries unnecessarily
β Forgetting CTE scope (only valid for one query)
Interview Tip π‘
> Subqueries solve problems inside queries, while CTEs solve readability and maintainability.
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β€1
π€ AβZ of Programming π»
A β Array
A data structure that stores a collection of elements of the same type, accessed by index.
B β Binary
A base-2 number system using 0s and 1s, the foundation of how computers represent data and perform operations.
C β Class
A blueprint in object-oriented programming for creating objects, defining attributes and methods.
D β Data Structure
An organization of data for efficient access and modification, like lists or trees.
E β Exception
An error or unexpected event during program execution that can be handled to prevent crashes.
F β Function
A reusable block of code that performs a specific task, often taking inputs and returning outputs.
G β Git
A version control system for tracking changes in code, enabling collaboration and history management.
H β HashMap/Hash Table
A data structure storing key-value pairs for fast lookups using hashing.
I β Inheritance
A mechanism where a class inherits properties and methods from a parent class in OOP.
J β JavaScript
A versatile language for web development, handling client-side interactivity and server-side with Node.js.
K β Keyword
A reserved word in a language with special meaning, like "if" or "for", not usable as variable names.
L β Loop
A control structure repeating code until a condition is met, such as for or while loops.
M β Modulus
An operator (%) returning the remainder of division, useful for cycles or checks.
N β Null
A special value indicating absence of data or no object reference.
O β Object
An instance of a class containing data (attributes) and behavior (methods) in OOP.
P β Pointer
A variable storing the memory address of another variable for direct access.
Q β Queue
A FIFO (First-In-First-Out) data structure for processing items in order.
R β Recursion
A function calling itself to solve smaller instances of a problem.
S β Stack
A LIFO (Last-In-First-Out) data structure, like a stack of plates.
T β Testing
Verifying a program's correctness through unit tests, integration, and more.
U β Unicode
A standard encoding characters from all writing systems for global text handling.
V β Variable
A named storage for data that can change during program execution.
W β While Loop
Repeats code while a condition remains true, offering flexible iteration.
X β XOR
A logical operator true if operands differ, used in cryptography and checks.
Y β Yield
A keyword returning a value from a generator, enabling lazy iteration.
Z β Zeroes (numpy.zeros)
Creates an array filled with zeros, useful for initialization.
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A β Array
A data structure that stores a collection of elements of the same type, accessed by index.
B β Binary
A base-2 number system using 0s and 1s, the foundation of how computers represent data and perform operations.
C β Class
A blueprint in object-oriented programming for creating objects, defining attributes and methods.
D β Data Structure
An organization of data for efficient access and modification, like lists or trees.
E β Exception
An error or unexpected event during program execution that can be handled to prevent crashes.
F β Function
A reusable block of code that performs a specific task, often taking inputs and returning outputs.
G β Git
A version control system for tracking changes in code, enabling collaboration and history management.
H β HashMap/Hash Table
A data structure storing key-value pairs for fast lookups using hashing.
I β Inheritance
A mechanism where a class inherits properties and methods from a parent class in OOP.
J β JavaScript
A versatile language for web development, handling client-side interactivity and server-side with Node.js.
K β Keyword
A reserved word in a language with special meaning, like "if" or "for", not usable as variable names.
L β Loop
A control structure repeating code until a condition is met, such as for or while loops.
M β Modulus
An operator (%) returning the remainder of division, useful for cycles or checks.
N β Null
A special value indicating absence of data or no object reference.
O β Object
An instance of a class containing data (attributes) and behavior (methods) in OOP.
P β Pointer
A variable storing the memory address of another variable for direct access.
Q β Queue
A FIFO (First-In-First-Out) data structure for processing items in order.
R β Recursion
A function calling itself to solve smaller instances of a problem.
S β Stack
A LIFO (Last-In-First-Out) data structure, like a stack of plates.
T β Testing
Verifying a program's correctness through unit tests, integration, and more.
U β Unicode
A standard encoding characters from all writing systems for global text handling.
V β Variable
A named storage for data that can change during program execution.
W β While Loop
Repeats code while a condition remains true, offering flexible iteration.
X β XOR
A logical operator true if operands differ, used in cryptography and checks.
Y β Yield
A keyword returning a value from a generator, enabling lazy iteration.
Z β Zeroes (numpy.zeros)
Creates an array filled with zeros, useful for initialization.
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π PYTHON TRICK #3
β Risky way:
data = {'name': 'Raj'}
age = data['age'] # KeyError! π₯
β Safe way:
age = data.get('age', 'Not found')
print(age) # "Not found"
β Even better:
age = data.get('age', 25) # Default value
π‘ No more KeyError crashes!
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β Risky way:
data = {'name': 'Raj'}
age = data['age'] # KeyError! π₯
β Safe way:
age = data.get('age', 'Not found')
print(age) # "Not found"
β Even better:
age = data.get('age', 25) # Default value
π‘ No more KeyError crashes!
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π§ SQL Interview Question (Commonly Asked)
π
customers(customer_id, customer_name)
orders(order_id, customer_id, order_date, order_amount)
β Ques :
π Find customers who have at least one high-value order (order_amount > 10,000) AND at least one low-value order (order_amount < 1,000).
𧩠How Interviewers Expect You to Think
β’ Classify rows using CASE WHEN
β’ Aggregate conditionally instead of filtering rows
β’ Ensure both conditions exist for the same customer
β’ Use HAVING with conditional counts
β’ Avoid filtering in WHERE which removes needed rows
π‘ SQL Solution
SELECT
c.customer_name
FROM orders o
JOIN customers c
ON o.customer_id = c.customer_id
GROUP BY c.customer_name
HAVING
SUM(CASE WHEN o.order_amount > 10000 THEN 1 ELSE 0 END) >= 1
AND SUM(CASE WHEN o.order_amount < 1000 THEN 1 ELSE 0 END) >= 1;
π₯ React β₯οΈ if you want more real-world SQL interview scenarios
π
customers(customer_id, customer_name)
orders(order_id, customer_id, order_date, order_amount)
β Ques :
π Find customers who have at least one high-value order (order_amount > 10,000) AND at least one low-value order (order_amount < 1,000).
𧩠How Interviewers Expect You to Think
β’ Classify rows using CASE WHEN
β’ Aggregate conditionally instead of filtering rows
β’ Ensure both conditions exist for the same customer
β’ Use HAVING with conditional counts
β’ Avoid filtering in WHERE which removes needed rows
π‘ SQL Solution
SELECT
c.customer_name
FROM orders o
JOIN customers c
ON o.customer_id = c.customer_id
GROUP BY c.customer_name
HAVING
SUM(CASE WHEN o.order_amount > 10000 THEN 1 ELSE 0 END) >= 1
AND SUM(CASE WHEN o.order_amount < 1000 THEN 1 ELSE 0 END) >= 1;
π₯ React β₯οΈ if you want more real-world SQL interview scenarios
OJECT: WHATSAPP BOT (Part 1)
Setup karte hain!
Library: pywhatkit
Install:
pip install pywhatkit
Basic code:
β οΈ WhatsApp Web logged in hona chahiye!
Part 2 tomorrow π
Tap β€οΈ , if you're interested in creating.
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Setup karte hain!
Library: pywhatkit
Install:
pip install pywhatkit
Basic code:
import pywhatkit
pywhatkit.sendwhatmsg(
"+919876543210",
"Hello from Python!",
15, 30 # Time: 3:30 PM
)
β οΈ WhatsApp Web logged in hona chahiye!
Part 2 tomorrow π
Tap β€οΈ , if you're interested in creating.
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π WHATSAPP BOT (Part 2)
Bulk messages bhejenge!
π‘ Use case:
β’ Business updates
β’ Birthday wishes
β’ Reminders
Questions? Ask! π¬
Tap β€οΈ , if you're creating.
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Bulk messages bhejenge!
import pywhatkit as kit
import time
contacts = [
"+919876543210",
"+919876543211"
]
message = "Sale Alert! 50% OFF"
for contact in contacts:
kit.sendwhatmsg_instantly(
contact, message
)
time.sleep(10)
π‘ Use case:
β’ Business updates
β’ Birthday wishes
β’ Reminders
Questions? Ask! π¬
Tap β€οΈ , if you're creating.
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β€3
π― LINKEDIN TIPS FOR DEVELOPERS
Profile optimize karo:
1οΈβ£ Headline:
"Python Developer | Automation Expert"
NOT "Student" or "Fresher"
2οΈβ£ Summary:
β’ What you do
β’ Skills (Python, pandas, etc)
β’ Projects (2-3 lines)
3οΈβ£ Featured:
β’ GitHub repos
β’ Projects
β’ Certificates
4οΈβ£ Post regularly:
β’ Python tips
β’ Mini tutorials
Clients LinkedIn se milte hain! πΌ for freelancing, for contractual jobs, for part time jobs
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Double Tap β₯οΈ For More
Profile optimize karo:
1οΈβ£ Headline:
"Python Developer | Automation Expert"
NOT "Student" or "Fresher"
2οΈβ£ Summary:
β’ What you do
β’ Skills (Python, pandas, etc)
β’ Projects (2-3 lines)
3οΈβ£ Featured:
β’ GitHub repos
β’ Projects
β’ Certificates
4οΈβ£ Post regularly:
β’ Python tips
β’ Mini tutorials
Clients LinkedIn se milte hain! πΌ for freelancing, for contractual jobs, for part time jobs
Share with Credit https://t.me/codingdidi
Double Tap β₯οΈ For More
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@Codingdidi
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β€1
π PYTHON TRICK #4
β Beginner way:
fruits = ['apple', 'banana']
for i in range(len(fruits)):
print(i, fruits[i])
β Pro way:
for i, fruit in enumerate(fruits):
print(i, fruit)
π‘ Cleaner + Pythonic!
Bonus with start:
for i, fruit in enumerate(fruits, start=1):
print(f"{i}. {fruit}")
Output:
1. apple
2. banana
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β Beginner way:
fruits = ['apple', 'banana']
for i in range(len(fruits)):
print(i, fruits[i])
β Pro way:
for i, fruit in enumerate(fruits):
print(i, fruit)
π‘ Cleaner + Pythonic!
Bonus with start:
for i, fruit in enumerate(fruits, start=1):
print(f"{i}. {fruit}")
Output:
1. apple
2. banana
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β€2
π― MINI CHALLENGE #3
Task: Filter Excel data
Given: sales.xlsx with columns:
β’ Name
β’ Department
β’ Salary
Filter:
1. Department = 'IT'
2. Salary > 50000
3. Save to new file
Use pandas!
β° 20 minutes
Solution tomorrow π
Hint: Use df[condition]
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Task: Filter Excel data
Given: sales.xlsx with columns:
β’ Name
β’ Department
β’ Salary
Filter:
1. Department = 'IT'
2. Salary > 50000
3. Save to new file
Use pandas!
β° 20 minutes
Solution tomorrow π
Hint: Use df[condition]
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β€2
β
CHALLENGE SOLUTION
π‘ Tips:
β’ Use & for AND
β’ Use | for OR
β’ Use () around conditions
Kisne solve kiya? π
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import pandas as pd
df = pd.read_excel('sales.xlsx')
filtered = df[
(df['Department'] == 'IT') &
(df['Salary'] > 50000)
]
filtered.to_excel(
'filtered_sales.xlsx',
index=False
)
print(f"Found {len(filtered)} records")
π‘ Tips:
β’ Use & for AND
β’ Use | for OR
β’ Use () around conditions
Kisne solve kiya? π
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β€3
π RESOURCE DROP!
Ultimate Python Cheatsheet:
π Data Types
π Loops & Conditions
π Functions
π File Handling
π Error Handling
π OOP Basics
π Common Libraries
π Best Practices
All in 1 PDF! π
Download link: [https://www.dataquest.io/wp-content/uploads/2024/09/Python-Cheat-Sheet.pdf]
πΎ Save kar lo!
Print karke desk pe rakho! π¨
Share with Credit https://t.me/codingdidi
Double Tap β₯οΈ For More
Ultimate Python Cheatsheet:
π Data Types
π Loops & Conditions
π Functions
π File Handling
π Error Handling
π OOP Basics
π Common Libraries
π Best Practices
All in 1 PDF! π
Download link: [https://www.dataquest.io/wp-content/uploads/2024/09/Python-Cheat-Sheet.pdf]
πΎ Save kar lo!
Print karke desk pe rakho! π¨
Share with Credit https://t.me/codingdidi
Double Tap β₯οΈ For More
β€1
Big Data Analytics (2024).pdf
8.4 MB
π Big Data Analytics
ββββββββββββββββββ
Theory, Techniques & Platforms
π¨βπ« Authors:
β’ Γmit Demirbaga
β’ Gagangeet Singh Aujla
β’ Anish Jindal
β’ OΔuzhan Kalyon
ββββββββββββββββββ
π What youβll learn:
ββββββββββββββββββ
β Big Data fundamentals
β Analytics concepts & theory
β Modern techniques & methodologies
β Big Data platforms & ecosystems
β Real-world analytical approaches
ββββββββββββββββββ
π― Who should read this?
ββββββββββββββββββ
β’ Data Analytics learners
β’ Big Data beginners
β’ Engineering / CS students
β’ Anyone exploring data-driven systems
ββββββββββββββββββ
π₯ Book shared below
ββββββββββββββββββ
Happy learning π
Stay consistent. Stay curious. π‘
ββββββββββββββββββ
Theory, Techniques & Platforms
π¨βπ« Authors:
β’ Γmit Demirbaga
β’ Gagangeet Singh Aujla
β’ Anish Jindal
β’ OΔuzhan Kalyon
ββββββββββββββββββ
π What youβll learn:
ββββββββββββββββββ
β Big Data fundamentals
β Analytics concepts & theory
β Modern techniques & methodologies
β Big Data platforms & ecosystems
β Real-world analytical approaches
ββββββββββββββββββ
π― Who should read this?
ββββββββββββββββββ
β’ Data Analytics learners
β’ Big Data beginners
β’ Engineering / CS students
β’ Anyone exploring data-driven systems
ββββββββββββββββββ
π₯ Book shared below
ββββββββββββββββββ
Happy learning π
Stay consistent. Stay curious. π‘
β€3
I rarely say this, but this is the best repository for mastering Python.
The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).
In this PythonMastery.pdf, all the information is structured
πΎ Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf
In the Exercises folder, all the exercises are located
πΎ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises
In the Solutions folder β the solutions
πΎ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions
Happy Learning!! π
The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).
In this PythonMastery.pdf, all the information is structured
πΎ Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf
In the Exercises folder, all the exercises are located
πΎ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises
In the Solutions folder β the solutions
πΎ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions
Happy Learning!! π
GitHub
python-mastery/PythonMastery.pdf at main Β· dabeaz-course/python-mastery
Advanced Python Mastery (course by @dabeaz). Contribute to dabeaz-course/python-mastery development by creating an account on GitHub.
β€1
Most developers are stuck in copy-paste mode
Thatβs why they never improve
So I simplified a viral project
into something you can actually understand
Inside:
β Clean 60-line code
β Step-by-step explanation
β Deep dive into how it really works
Donβt just run it
Understand it
Code Explanation: https://www.youtube.com/watch?v=O1kff3z4XhU
Code reference: https://github.com/Akansha-yadav24/hand-draw-opencv/blob/main/README.md
Then understand deeply: https://shorturl.at/cY8Rt
Share with Credit https://t.me/codingdidi
Double Tap β₯οΈ For More
Thatβs why they never improve
So I simplified a viral project
into something you can actually understand
Inside:
β Clean 60-line code
β Step-by-step explanation
β Deep dive into how it really works
Donβt just run it
Understand it
Code Explanation: https://www.youtube.com/watch?v=O1kff3z4XhU
Code reference: https://github.com/Akansha-yadav24/hand-draw-opencv/blob/main/README.md
Then understand deeply: https://shorturl.at/cY8Rt
Share with Credit https://t.me/codingdidi
Double Tap β₯οΈ For More
YouTube
I Built This Viral AI Project in 60 Lines (Full Breakdown)
Most people are building this viral air drawing project.
Very few actually understand it.
In this video, I break it down from scratch.
No shortcuts. No copy-paste thinking.
Just ~60 lines of code.
And complete clarity.
Youβll learn how this actually works:β¦
Very few actually understand it.
In this video, I break it down from scratch.
No shortcuts. No copy-paste thinking.
Just ~60 lines of code.
And complete clarity.
Youβll learn how this actually works:β¦
Opportunity alert for my community Machine Learning Engineer at Grid Dynamics Bengaluru, Karnataka, IN
I've been curating the best opportunities for you all, and this one is worth checking out. If you've been looking to level up, this could be it.
Apply here: https://artha.link/@codingdidi/jobs/machine-learning-engineer-grid-dynamics-bengaluru-8a40a954
I've been curating the best opportunities for you all, and this one is worth checking out. If you've been looking to level up, this could be it.
Apply here: https://artha.link/@codingdidi/jobs/machine-learning-engineer-grid-dynamics-bengaluru-8a40a954
artha.link
Machine Learning Engineer
Total Experience- 4-6 years NP- Immediate-15 days Location- Bangalore A mid-level ML/Deep-Learning engineer who can do model development & training on text data... | artha.link
Job title: Machine learning engineer (Gen AI)
Location: Bangalore
experience: 4 To 7Years
Key responsibilities
- Advise clients on GenAI use cases, strategies, and provide feedback for product roadmap
- Evaluate and recommend suitable GenAI models and solutions for clientβs problem statement
- Design and prototype GenAI features and applications, focusing on user needs and value
- Keep up with the latest advancements in AI technologies, including Gen AI and the latest features of popular tools (RAG)
- Collaborate with engineering and design teams to ensure successful product integration
- Collect, clean, and preprocess data using appropriate tools and libraries, ensuring compatibility with AI algorithms and frameworks
- Train, test, and evaluate AI models employing appropriate evaluation metrics
- Optimize and fine-tune models for performance, scalability and efficiency
- Implement and deploy AI solutions in production environments under different frameworks
- Ability to build applications and use appropriate prompting on generative AI models
- Strong experience in Natural Language Processing, Large Language Models, and RAG approaches
- Assess the ethical implications and risks associated with GenAI product deployments
- Support pre-sales activities by providing GenAI expertise and contributing to product roadmaps
Skills and attributes for success - To qualify for the role you must have
- Bachelor's /Master's degree in computer science, data science, engineering, or a related field.
- Strong hands-on background in AI, machine learning, deep learning, and statistical modeling
- Proficiency in programming languages such as Python, Java
- Strong understanding of existing GenAI services
- Strong understanding of data structures, algorithms and AI project lifecycle
Apply Link: https://artha.link/@codingdidi/jobs/machine-learning-engineer-edgeverve-bangalore-ddc7f30c
Location: Bangalore
experience: 4 To 7Years
Key responsibilities
- Advise clients on GenAI use cases, strategies, and provide feedback for product roadmap
- Evaluate and recommend suitable GenAI models and solutions for clientβs problem statement
- Design and prototype GenAI features and applications, focusing on user needs and value
- Keep up with the latest advancements in AI technologies, including Gen AI and the latest features of popular tools (RAG)
- Collaborate with engineering and design teams to ensure successful product integration
- Collect, clean, and preprocess data using appropriate tools and libraries, ensuring compatibility with AI algorithms and frameworks
- Train, test, and evaluate AI models employing appropriate evaluation metrics
- Optimize and fine-tune models for performance, scalability and efficiency
- Implement and deploy AI solutions in production environments under different frameworks
- Ability to build applications and use appropriate prompting on generative AI models
- Strong experience in Natural Language Processing, Large Language Models, and RAG approaches
- Assess the ethical implications and risks associated with GenAI product deployments
- Support pre-sales activities by providing GenAI expertise and contributing to product roadmaps
Skills and attributes for success - To qualify for the role you must have
- Bachelor's /Master's degree in computer science, data science, engineering, or a related field.
- Strong hands-on background in AI, machine learning, deep learning, and statistical modeling
- Proficiency in programming languages such as Python, Java
- Strong understanding of existing GenAI services
- Strong understanding of data structures, algorithms and AI project lifecycle
Apply Link: https://artha.link/@codingdidi/jobs/machine-learning-engineer-edgeverve-bangalore-ddc7f30c
artha.link
Machine Learning Engineer
Job title: Machine learning engineer (Gen AI) Location: Bangalore experience: 4 To 7Years Key responsibilities Advise clients on GenAI use cases, strategies, an... | artha.link
β€1
Weβre Hiring: DevOps Engineer (Lead / Intermediate) | Toronto (Hybrid)
Looking for your next DevOps opportunity? Weβre hiring a hands-on DevOps Engineer to join a leading banking client in Toronto. If you enjoy building CI/CD pipelines and working on real-world deployments, this could be a great fit.
Location: Toronto (Bay Street) β Hybrid (2 days onsite/week)
Type: Contract (12 months + extensions)
Rate: Market rate (40 hours/week)
Start Date: Immediate
What Youβll Do:
β’ Build GitHub Actions workflows from scratch
β’ Develop CI/CD pipelines for Java / Spring Boot applications
β’ Deploy and manage applications on OpenShift (Kubernetes)
β’ Collaborate with DevOps and development teams
β’ Troubleshoot and optimize deployment pipelines.
What Weβre Looking For:
β’ 2β5 years of experience (Intermediate to Lead level)
β’ Strong hands-on experience with GitHub Actions (must-have)
β’ Experience with Java / Spring Boot
β’ Hands-on exposure to OpenShift / Kubernetes
β’ CI/CD pipeline development experience
β’ Knowledge of Docker and Linux/Unix
β’ Cloud exposure (AWS/Azure) is a plus.
Ideal Candidate:
β’ Hands-on contributor with a problem-solving mindset
β’ Takes ownership and thrives in a fast-paced environment
β’ Eager to learn and grow
Interested?
DM me or email your resume to priya@csican.com with details of your experience in GitHub Actions, Spring Boot, and OpenShift.
Looking for your next DevOps opportunity? Weβre hiring a hands-on DevOps Engineer to join a leading banking client in Toronto. If you enjoy building CI/CD pipelines and working on real-world deployments, this could be a great fit.
Location: Toronto (Bay Street) β Hybrid (2 days onsite/week)
Type: Contract (12 months + extensions)
Rate: Market rate (40 hours/week)
Start Date: Immediate
What Youβll Do:
β’ Build GitHub Actions workflows from scratch
β’ Develop CI/CD pipelines for Java / Spring Boot applications
β’ Deploy and manage applications on OpenShift (Kubernetes)
β’ Collaborate with DevOps and development teams
β’ Troubleshoot and optimize deployment pipelines.
What Weβre Looking For:
β’ 2β5 years of experience (Intermediate to Lead level)
β’ Strong hands-on experience with GitHub Actions (must-have)
β’ Experience with Java / Spring Boot
β’ Hands-on exposure to OpenShift / Kubernetes
β’ CI/CD pipeline development experience
β’ Knowledge of Docker and Linux/Unix
β’ Cloud exposure (AWS/Azure) is a plus.
Ideal Candidate:
β’ Hands-on contributor with a problem-solving mindset
β’ Takes ownership and thrives in a fast-paced environment
β’ Eager to learn and grow
Interested?
DM me or email your resume to priya@csican.com with details of your experience in GitHub Actions, Spring Boot, and OpenShift.
β€1