DSA With Python Free Resources

Design and Analysis of Algorithms
🆓 Free Video Lectures
📒 Lecture Notes + Assignments with Solutions + Exams with their Answers
⏰ Duration: 40 hours
🏃‍♂️ Self Paced
📈 Difficulty: Advanced
👨‍🏫 Created by: MIT OpenCourseWare
🔗 Course Link

Data Structures and Algorithms in Python Full course
🆓 Free Online Course
⏰ Duration : ~13 hours
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Instructor: Aakash N S
🔗 Course Link

Data Structures & Algorithms in Python
🎬 Free Video Lectures
⏰ Duration: 1 hour
🏃‍♂️Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Simplilearn
🔗 Course Link

The Algorithms - Python
📚 500+ algorithms
🏃‍♂️ Self Paced
📈 Difficulty: All Levels
👨‍🏫 Created by: Community(Open-source)
🔗 Course Link

Data Structures and Algorithms
🆓 Free Video Series
⏰ Duration: 4 hours
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: CS Dojo
🔗 Course Link

Python Data Structures
📚 Complete Course
🏃‍♂️Self Paced
📈Difficulty: Basic - Intermediate
👨‍🏫 Created by: prabhupant
🔗 Course Link

Reading Resources

📖 DSA with Python
📖 Problem Solving with Algorithms
📖 Algorithm Archive
📖 Python DSA

#DSA #Python
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👨‍🏫 The Zero to Mastery Python Cheat Sheet is a clean, colorful cheatsheet packed with practical code snippets for everyday tasks like loops, functions, and list comprehension.

🤩 It’s visually organized with clear sections and real examples, which makes it a favorite for beginners and intermediates who want to code faster and smarter.

Decorators Are Not Magic. They’re Callbacks in Disguise

You’ve used @lru_cache to speed up a slow function, and it worked... until your app started eating RAM because the cache never forgot anything.

from functools import lru_cache

@lru_cache
def fib(n):
return fib(n-1) + fib(n-2) # ← Cache grows forever!

👉Here’s what’s really happening:
A decorator is just a function that wraps another function. When you write @lru_cache, Python replaces your fib with a new version that remembers every answer it’s ever given. Cool😄 until n goes from 1 to 100,000.

✅ Fix it like a pro:
from functools import lru_cache

@lru_cache(maxsize=128) # Only keep last 128 results
def fib(n):
if n > 1000:
return manual_calc(n) # Skip cache for huge inputs
return fib(n-1) + fib(n-2)

Now the cache stays small, predictable, and safe.

📌Bonus: Write your own @timerdecorator in 5 lines. no more time.time() spam.

🔥 Python vs SQL: Who Cleans Data Better? 🧹

Python Data Structures: Quick Visual Guide 🐍

🔹 Lists: Ordered, mutable, created with [ ]
→ Access/modify via index: myList[0], myList[-1]
→ Methods: .append(), .sort(), .pop()
→ Mixed types allowed
→ Loop: for item in myList:

🔹 Tuples: Immutable, ordered → (1, 2, 3)

🔹 Sets: Unordered, unique elements

🔹 Dictionaries: Key-value pairs, fast lookups

🔹 Arrays: Mainly for numeric data (array/NumPy)

🔑 Key Points:
✅ Indexing: 0 to len-1 (forward), -1 backward
✅ Assignment myList[i] = x modifies in place
✅ Lists are the most versatile & commonly used

This is the perfect cheat sheet for beginners and for quick revision!

Put your answers in the comment below🔽

FREE Courses On Python Asyncio

Advanced asyncio: Solving Real-World Production Problems
🆓 Free Video Course
⏰ Duration: 41 Min
🏃‍♂️ Self paced
📊 Difficulty: Advanced
👨‍🏫 Created by: PyVideo
🔗 Course Link

Async IO Basics
🆓 Free Online Course
⏰ Duration: ~22 minutes
🏃‍♂️ Self paced
📊 Difficulty: Beginner
👨‍🏫 Created by: Very Academy
🔗 Course Link

Asyncio in Python - Full Tutorial
🆓 Free Video Course
⏰ Duration: 25 Min
🏃‍♂️ Self paced
📊 Difficulty: Beginner
👨‍🏫 Created by: Tech with Tim
🔗 Course Link

Asyncio Basics - Asynchronous programming with coroutines
🆓 Step-by-step text + video
⏰ Duration: 25 Min
🏃‍♂️ Self paced
📊 Difficulty: Beginner - Intermediate
👨‍🏫Created by: Python Programming Tutorials
🔗 Course Link


Reading Materials

📖 Python's Ayncio
📖 Asyncio Tutorial for Beginners
📖 Python Asyncio: The Complete Guide
📖 Official Asyncio Docs
📖 Asyncio Learning Path


#python  #asyncio
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What is Walrus Operator (:=) in Python?

Important Python Function and their Purpose

Put your answers in the comment below🔽

Concise reference compiled from Stack Overflow Q&A covering syntax, OOP, modules, error handling, and advanced topics like decorators.

Decorators in Python

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Python Roadmap For AI/ML

Python For Data Science Cheatsheet: Part 1

Python For Data Science Cheatsheet: Part 2

Image Caption Generator

Multimodal AI: CNN-RNN combo generates denoscriptive captions for images (e.g., "dog chasing ball"). Showcases encoder-decoder architectures.

🔗 Repo Link: https://github.com/yunjey/show-attend-and-tell


#PythonProjects  #ImageCaptionGenerators

Put your answers in the comment below!🔽