Machine Learning with Python Guide

🚀 Day 3 — Python Journey Today’s focus was on float operations in Python (working with decimal numbers). 📌 What I learned: Float declaration Addition, subtraction, multiplication, division Rounding values using round() Scientific notation Precision handling in floats 💡 What I found interesting: Float values are not always 100% accurate due to precision limitations. Even simple calculations can sometimes give unexpected results. Understanding this early is important, especially for real-world applications like finance or data science. Step by step, trying to build a strong foundation. #Day3 #Python #CodingJourney #LearnInPublic #Consistency

🚀 Built a simple Day/Night image classifier from scratch using Python. This project is a simple machine learning pipeline built from scratch that classifies images as day or night using handcrafted features and a custom linear SVM. 💾 Source code: https://lnkd.in/dp9kmRmN 💻 Tutorial: https://lnkd.in/d5epPRnt

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Day 1 of Data Structures in Python 🚀 Today I learned the basics of: • Lists • Tuples • Sets • Dictionaries Practiced few basic operations like insert, delete, and search. Understanding how data is stored and accessed is the first step toward better problem-solving. Looking forward to applying these concepts in real problems 🔍 #Python #DSA #LearningJourney #DataStructures

Back to consistency 💻🚀 Recently, I worked on implementing Pascal’s Triangle in Python — and it turned out to be a great exercise in logic building. While solving this, I learned: 🔹 How each row depends on the previous one 🔹 Better understanding of nested loops 🔹 Using mathematical logic instead of brute force It’s interesting how such a simple-looking pattern involves deeper thinking behind the scenes. Here’s my implementation 👇 Small steps like these are helping me build a strong foundation in Data Structures & Algorithms. #Python #DSA #CodingJourney #LearningInPublic #100DaysOfCode

Dove into working with files in Python — from accessing and importing text files to actually parsing and making sense of the data inside them. It’s one of those things that seems simple at first… until you realize how powerful it actually is. There’s something satisfying about going from “just reading a file” to actually extracting useful information from it.

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Creating example datasets has never been this easy. With the drawdata library in Python, you can sketch your data and turn it into a dataset in seconds. You can create clusters, trends, and outliers exactly the way you need. I just released a new module on this in the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #datascience #python #machinelearning #statistics #dataanalysis #datavisualization #programming #ai #statisticsglobehub

🚀 Day 11/30 – Python Challenge Exploring sets in Python and how they handle unique data! 🐍 🔹 Key Concepts Covered: * Creating sets * Understanding that sets store only unique values * Adding elements using add() * Iterating through set elements 💻 Mini Task: Created a set of numbers, observed how duplicate values are automatically removed, added a new element, and displayed all values using a loop. 🎯 Learning Outcome: Learned how sets are useful for storing unique data and performing operations where duplicates are not needed. Understanding different data structures step by step 🚀 #Python #CodingChallenge #LearningJourney #DataStructures #StudentDeveloper #Day11