#Day52 of my fourth #100DaysOfCode Working on a collection of Python projects, including: 1. Scientific Calculator: A simple scientific calculator built with Python's tkinter library, featuring basic arithmetic operations, trigonometric functions, and more! 2. Scrabble Game: A multiplayer Scrabble game built with Python's tkinter library, featuring word validation, score tracking, and definition lookup. 3. Screen Capture Tool: A screen capture tool built with Python's OpenCV and PIL libraries, allowing for screen recording, screenshot capture, and delayed screenshot capture. 4. Search Bot: A search bot built with Python's requests and BeautifulSoup libraries, featuring search query input, search engine selection, summary generation, and word cloud generation. Check out the code on GitHub let me know what you think! Github Link:- https://lnkd.in/gsTcpcAw #Python #Coding #Projects #Scrabble #Calculator #ScreenCapture #SearchBot #OpenCV #tkinter #BeautifulSoup #requests #PIL #gensim #wordcloud #matplotlib #nltk #PyDictionary

🚀 Today I Learned: Efficiently Finding Top K Frequent Elements in Python! I was working on the LeetCode problem “Top K Frequent Elements” and explored Bucket Sort as an alternative to traditional sorting. Instead of sorting all elements (O(n log n)), I used a bucket-based approach: 1️⃣ Count the frequency of each number 2️⃣ Create a bucket array where each index represents frequency 3️⃣ Place numbers into their corresponding frequency bucket 4️⃣ Collect top K elements starting from the highest frequency This method is O(n) in time complexity — much faster for large datasets! 💡 Key takeaway: Multiple numbers with the same frequency can coexist in the same bucket. The order inside the bucket doesn’t matter — the problem allows any order. Here’s a snapshot of my code and my learning today! 📸 #Python #Coding #DataStructures #Algorithms #LeetCode #Learning #FullStackDeveloper #ProblemSolving NeetCode

🚀 Exploring Gradient Descent with Python – 2D Visualization 🎨 I just built an interactive 2D gradient descent visualization in Python to better understand how optimization works! 💡 What it does: Shows a ball moving along the curve y=x2 following gradient descent steps. Includes a descent line connecting each step to illustrate progress. Interactive starting position slider to see how different initial points affect convergence. Symbolic derivatives via SymPy for accurate gradient computation. 📊 Why it’s cool: Makes gradient descent intuitive and visual, especially for beginners in machine learning or optimization. Helps explore concepts like learning rate, convergence speed, and initialization effects. 🔥 Tech Stack: Python | NumPy | SymPy | Matplotlib | Matplotlib Animation 💻 GitHub Repository: https://lnkd.in/dW_TqEBi This project was a great hands-on way to connect theory with visualization. Next, I’m planning to extend it to custom functions and gradient ascent for a full interactive learning experience! #Python #MachineLearning #DataScience #Matplotlib #SymPy #OpenSource #Visualization #GradientDescent

Just stumbled on tinker-cookbook – a Python repo that shows how to do post‑training tricks with Tinker 🔧. It’s basically a recipe book of fine‑tuning steps, model‑compression hacks and quick eval scripts that save hours of boilerplate. With 1,949 stars the community already loves it 🚀, and the notebooks are super clean. If you’re tinkering with LLMs or any PyTorch model, give it a look – you’ll pick up ready‑made pipelines you can drop into your own code. https://lnkd.in/dnPHDzbJ #Python #Programming #thinking-machines-lab #MachineLearning #AI #Trending

Today I explored NumPy, one of the most powerful library of Python for numerical and scientific computing. Here’s what I practiced: Creating arrays with np.array() Using functions like zeros(), ones(), arange(), eye(), and linspace() Checking dimensions with .ndim Understanding array shapes using .shape I’m really enjoying how NumPy makes working with data so much easier and faster. #Python #NumPy #DataScience #LearningJourney #PythonForDataScience

🚀 Just wrapped up a deep‑dive into NumPy and Python functions! 📊💻 🔹 Built arrays, checked shapes & dimensions, and explored broadcasting. 🔹 Wrote reusable functions – from Fibonacci & grade calculators to life‑phase checkers. 🔹 Played with random data, slicing, and basic stats (mean, var, std). Big shout‑out to the open‑source community for making data‑science so approachable. #Python #NumPy #DataScience #Coding #LearningInPublic

Iris Flower Classification using Machine Learning Excited to share my latest hands-on project where I trained and tested a Random Forest Classifier on the Iris dataset using Python and scikit-learn! 🔹 The first notebook focuses on quick model training and testing 🔹 The second notebook calculates and verifies accuracy This project highlights the end-to-end ML workflow — from data preprocessing to model evaluation. 💻 View the complete code and notebooks on my GitHub Repository here: https://lnkd.in/gtyUV7-Z #MachineLearning #Python #DataScience #ArtificialIntelligence #MLProjects #IrisDataset #ScikitLearn #RandomForest #OpenSource #GitHubProjects

💡 Creating Arrays using NumPy! Just explored how to create and manipulate different types of arrays using NumPy — from 1D to multidimensional arrays, along with functions like arange(), zeros(), ones(), and linspace(). This helped me understand how NumPy makes numerical operations faster and more efficient. ⚡📊 🔗 GitHub Repository: https://lnkd.in/gsPj_hxs 👨🏫 Under the guidance of: Ashish Sawant #NumPy #Python #DataScience #MachineLearning #CodingJourney #LearningEveryday

Diving deep into Python and unlocking its true power! 💡 Just wrapped up learning about loops, functions, and NumPy in Python with Digital Skola. Such a fundamental and powerful set of tools! - Loops: Harnessing for and while loops to automate repetitive tasks, from processing data to cleaning datasets. - Functions: Writing reusable functions for better code modularity and readability. - NumPy: Exploring the power of NumPy for lightning-fast numerical computation. Handling large arrays for data analysis and machine learning just became a whole lot easier and more efficient. This is a powerful combination for anyone looking to level up their Python skills. Wanna know more about these topics, let's check this slide below 👇 #DigitalSkola #LearningProgressReview #DataScience

🗓 Day 2 / 100 – #100DaysOfLeetCode 📌 Problem 1636: Sort Array by Increasing Frequency The task was to sort an array such that elements with lower frequency appear first, and if two elements have the same frequency, the larger number comes first. 🧠 My Approach: Counted element frequencies using a hash map. Sorted the elements by ascending frequency and then by descending value. Reconstructed the array based on sorted frequency order. ⏱ Time Complexity: O(n log n) 💾 Space Complexity: O(n) 💡 Key Learning: This problem reinforced how powerful custom sorting logic can be in Python, especially when handling multiple sort priorities using tuple-based keys in sorting functions. Each day is helping me refine how I think about data organization, sorting, and frequency analysis — small steps that build strong foundations. #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #Algorithms #DataStructures #DSA #Sorting #CodingJourney #CodingChallenge #SoftwareEngineering #CompetitiveProgramming #CodeEveryday #LearningInPublic #DeveloperJourney #TechStudent #CareerGrowth #CodingCommunity #KeepLearning