Day 37 / #120DaysOfCode – LeetCode Challenge ✅ Problem Solved: • Search a 2D Matrix 💻 Language: Python 📚 Key Learnings: • Applied Binary Search on a 2D matrix • Learned how to treat matrix as a flattened sorted array • Practiced converting 1D index → 2D index (row, col) • Improved understanding of search space reduction • Strengthened logarithmic time complexity (O(log n)) thinking Better logic → Faster execution 🚀 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #BinarySearch #Algorithms #CodingJourney #Consistency #120DaysOfCode

Today, I started diving into the basics of Python, the programming language at the heart of AI and Machine Learning. I explored different data types like integers, floats, booleans, complex numbers, and strings, and learned the rules for using parentheses and other syntax essentials. My Key Takeaways: Choosing the right data type is critical for correct operations Understanding Python syntax ensures your code runs smoothly These foundational concepts make everything else in AI/ML easier to learn Python may seem simple at first glance, but mastering the basics is the first step to building complex AI solutions. #Python #AI #MachineLearning #DataScience #30DayChallenge #M4ACE

I built my own simple RAG (Retrieval-Augmented Generation) system using React, Flask, and Python: ✨ Features: A landing page where I added a block of information. That information is saved as a PDF and extracted using PyMuPDF. Users can ask questions, and the system retrieves answers using Chroma DB vectorization and chunking. 📹 Check out the demo video below 👇 This project was a great hands-on way to understand RAG workflows, vector databases, and PDF text extraction. #Python #ReactJS #Flask #RAG #ChromaDB #PyMuPDF #AI #WebDevelopment

🚀 Solved: Find a String (Substring Count) Challenge Just solved another problem on HackerRank under the Python Strings section! ✅ 🧠 Problem Overview: Count how many times a substring appears in a string — including overlapping occurrences. 🔍 Key Learnings: Practiced string traversal techniques Understood why built-in methods like count() may not always work (no overlapping support) Strengthened concepts of slicing and iteration in Python 💡 Example Insight: For string "ABCDCDC" and substring "CDC", the answer is 2 (overlapping counts matter!). ⚡ Approach Used: Iterated through the string Compared substrings using slicing Counted valid matches efficiently 📈 Problems like this help build strong fundamentals in string manipulation, which is crucial for coding interviews and real-world applications. #Python #HackerRank #Coding #Strings #ProblemSolving #DSA #LearningJourney #AI link of #Solution :- https://lnkd.in/gtqcy8fX

Week 1 – Learning Progress in Generative AI 🚀 This week I focused on: Python fundamentals for data handling Working with libraries like pandas, numpy and matplotlib Setting up the development environment in VS Code Key takeaway: Understanding the environment setup and libraries is just as important as writing code. Small setup issues can slow you down, but solving them builds confidence. Looking forward to diving deeper into real-world data problems next. #GenerativeAI #Python #LearningJourney #CareerTransition

🎬 Built a Movie Recommendation System using Python & ML Using content-based filtering to recommend movies based on similarity — here's a quick breakdown how it works: 1. TF-IDF Vectorizer converts movie descriptions into vectors. 2. Cosine Similarity measures how similar two movies are. 3. Random Forest classifier validates the results. Results: ◈ 16 movies ◈ 4 genres ◈ 97% model accuracy ◈ Toy Story & Finding Nemo topped similarity at 0.61 ◈ The Godfather & Goodfellas closely matched at 0.58 A great way to understand how Netflix-style recommendations work under the hood. Open to feedback and questions! 👇 #MachineLearning #Python #DataScience #RecommendationSystem #BuildInPublic #dataanalytics #datascience #mlproject #datanalyst #datascientist #scikitlearn

🚀 Day 2 of my AI & Data Science Journey Today I learned some important basics of Python 🐍 • What are Data Types (int, float, string, boolean) • How to use Variables to store values • Different types of Operators • Type Casting (converting one data type into another) Slowly understanding how coding actually works 💻 Small steps, but moving forward every day 📈 #Day2 #Python #LearningJourney #DataScience #Beginner #Consistency #AI

Recently I attended a webinar on Python 🐍 with AI 🧠 by TOPS Technologies 🧑💻 . Honestly, it was a really interesting session 👌. 👉 I got to understand how Python is actually used in AI and real-world applications. ✍ Still learning, but moving step by step 👍 #Python #AI #LearningJourney

🚀 Day 6/30 – Python Challenge Exploring loops in Python today! 🐍 🔹 Key Concepts: * for loop using range() * while loop execution * Iteration and repetition in programs 💻 Mini Task: Printed numbers from 1 to 5 using both for loop and while loop to understand their working. 🎯 Learning Outcome: Learned how loops help automate repetitive tasks and make code more efficient. Consistency + practice = improvement 📈 #Python #CodingChallenge #LearningJourney #AI #StudentDeveloper #Day6

🚀 Day 5 of My Generative & Agentic AI Journey! Today’s focus was on understanding Tuples in Python and how they work. Here’s what I learned: 🔗 Tuples in Python: • Tuples are denoted using () brackets • They are immutable — once created, they cannot be changed • Useful for storing fixed data 🔄 Swapping Values: • Learned a very clean Python trick to swap values • Example: A, B = 2, 1 • Swap using: A, B = B, A 🔍 Checking Elements: • Used the “in” keyword to check if an element exists in a tuple 👉 Key takeaway: Tuples are simple, efficient, and useful when you don’t want your data to change. Slowly building strong Python fundamentals step by step 💪 #Day5 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic