Developed a machine learning model to classify Iris flowers into Setosa, Versicolor, and Virginica based on their measurements. This project helped me gain hands-on experience in data preprocessing, model training, and evaluation using Python and Scikit-learn. #oasisinfobyte#MachineLearning #DataScience #IrisClassification #Python #ScikitLearn #LearningJourney #MLProjects

Diving deeper into Python Strings 🐍 Today’s class focused on working with strings and understanding how Python handles text data. Key learnings from the session: • String slicing using positive and negative indexing • Extracting substrings with custom step values • Using len() to find the length of strings • Handling empty strings and undefined variables • Understanding and fixing NameError and SyntaxError • Using the count() method to find occurrences of characters, words, and patterns in a string • Applying string operations on real examples like sentences and date formats These concepts are small but powerful and play a big role in text processing and data handling. Enjoying the process of learning by practicing and making mistakes along the way 🚀 #Python #StringManipulation #LearningPython #Codegnan #ProgrammingJourney #Consistency Pooja Chinthakayala

🌸 TASK -3 Iris Flower Classification | Machine Learning Project Built a Random Forest Classifier using Python to classify Iris species. Performed EDA, feature encoding, model training, and evaluation — achieving 100% accuracy. CodSoft github link : https://lnkd.in/gVJMx7Jy go to check.. ❤ #MachineLearning #DataScience #Python #RandomForest #EDA#codsoft

#Day19 of my Data Science and Machine Learning journey at Skill Shikshya Today I went deeper into exception handling in Python. This is one of those topics people ignore until their code breaks in real projects. What I learned today: ✔️ Try and except blocks to handle runtime errors safely ✔️ Raise to create custom exceptions when something goes wrong ✔️ Why proper error handling makes programs more stable and easier to debug If you do not handle errors properly, your program will crash at the worst possible time. Learning this now is necessary, not optional. Consistency over speed. #100DaysOfLearning #Python #DataScience #MachineLearning #SkillShikshya #LearningJourney

We know that creating knowledge graphs from unstructured data can be a headache. Now? The Neo4j GraphRAG for Python package includes a Knowledge Graph Builder to help you convert your unstructured and structured data. The result: Organized representations of real-world entities and relationships that power better #AI applications. Read more about this ⬆️ in the blog and learn how to create them in the #GraphAcademy course "Constructing Knowledge Graphs with Neo4j GraphRAG for Python." https://bit.ly/4pRh6iK #Python #Neo4j

Today I Learnt: The Power of Binary Search! 💡 I’ve been diving deeper into Python algorithms today, specifically focusing on Binary Search. While a simple linear search checks every single element one by one, Binary Search uses a "divide and conquer" strategy. By constantly splitting the search area in half, it achieves a time complexity of O(\log n), making it incredibly fast for large, sorted datasets. Pre-requisite: The data must be sorted. Logic: It compares the target value to the middle element and eliminates half of the remaining data in every step. Implementation: Used a while loop to dynamically adjust the start and end pointers. It’s amazing to see how a few lines of logic can drastically optimize performance! #Python #CodingLife #Algorithms #BinarySearch #SoftwareEngineering #TodayILearned

🚀 Just pushed a new machine learning implementation to GitHub! I built **Multiple Linear Regression from scratch** using **vectorized gradient descent in Python/NumPy** to compare performance with traditional loops. Vectorization makes ML code *dramatically faster* by leveraging optimized C/Fortran kernels and SIMD instructions! 🧠💡 :contentReference[oaicite:1]{index=1} 💻 Repository: https://lnkd.in/gppzrgrn 📌 Highlights: ✅ Fully vectorized linear regression training ✅ Gradient descent implemented from first principles ✅ Demonstrated performance improvement over loop‑based code ✅ Clear explanation and concepts inside README If you're learning ML fundamentals or want to see how vectorization boosts efficiency in numerical code, check it out! #MachineLearning #Python #NumPy #GradientDescent #Vectorization #DataScience #MLfromScratch #ANDREWNG

🚀 Day 5 of my Python → AI/ML Journey Today was about strengthening the basics, not rushing ahead. 🔍 What I practiced: Python functions print() vs return Using loops inside functions Solving small logic problems Mini menu-based Student Marks program (loops + lists + functions) 💡 Key learning: Strong basics matter. Clean logic today = faster growth in AI/ML tomorrow. 📌 One concept at a time. One step forward. #Day5 #Python #LearningPython #Functions #LogicBuilding #AIML #DataScience #MCAJourney #CodingFromScratch #Consistency

Learning Multiple Linear Regression as part of my Machine Learning fundamentals. This video showcases a hands-on implementation of gradient descent for multi-feature data, focusing on understanding cost minimization and parameter updates from the ground up. Building strong fundamentals through practical implementation and continuous learning. #MachineLearning #MultipleLinearRegression #DataScience #Python #GradientDescent #LearningByDoing #MLBasics

Strengthening Analytics Foundations – Day IV Today’s learning focused on strings in Python—creating and accessing strings, performing string operations, and understanding indexing (positive and negative) and slicing. A useful reminder: in real-world datasets, much of the complexity lies in text data—names, locations, identifiers, and codes. Clean, well-handled strings are essential for accurate analysis, matching, and reporting. #DataAnalytics #Python #DataQuality #PublicSector #ContinuousLearning