Solomon Theuri’s Post

🧠 Fine-Tuning Transformer for Sentiment Analysis with Hugging Face 🔥 What’s the point? ⮑ Fine-tuning is all about adapting a pretrained model to perform better on specific tasks or datasets—in this case, analyzing sentiment. ⮑ By leveraging Hugging Face’s Transformers library, we can customize models like BERT to identify emotions and opinions with higher accuracy. ⮑ This tuning aligns the model more closely with the nuances of your unique data, leading to more relevant sentiment insights. 👉🔗 Try now: https://lnkd.in/dt4f8z86 #machinelearning #huggingface #artificialintelligence #python

🔍 Perceptron Algorithm in Action! 🧠 I just implemented the Perceptron algorithm from scratch in Python using synthetic classification data. In this decision boundary visualization and an animated learning process. Key highlights: Custom Perceptron implementation. Dynamic decision boundary updates. Data visualization with matplotlib. Check it out on [https://lnkd.in/gR--pyY5]! 💻 #MachineLearning #Python #AI #DataScience

#day_246 #medium #leetcode_537- Complex Number Multiplication (https://lnkd.in/gnTSPs9q) Given: Two strings To do: Write a code to find the complex number's multiplication. My Solution:   1. Split the complex number strings which separate the real and imaginary parts. 2. Convert the real and imaginary parts (excluding the i) into integers. 3. Apply the formula for multiplying two complex numbers.  Time Complexity : O(1)  Space Complexity : O(1) my solution link -  https://lnkd.in/g2wMJV5a #leetcode #python #dsa #math #string #problemsolving

TASK 1: (Unemployment Rate) In this task, we utilized Matplotlib and Seaborn in Python for data analysis through visualizations. We explored trends, correlations, and distributions within the dataset. By generating line charts, bar plots and more, we gained insights into temporal patterns, and relationships between variables, aiding informed decision-making. #oasisinfobyte #oasisinfobyteinternship

🚨𝗦𝗶𝗺𝗽𝗹𝗲 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗔𝗹𝗲𝗿𝘁🚨 In the comments🔽, you'll find a 🔗 to my Kaggle notebook where I demonstrate the application of Simple Linear Regression from scratch, without using any libraries. This approach helps in gaining a clearer statistical and logical understanding of the algorithm. What you can expect: 🔎Plots with Plotly: Visualizations to illustrate the regression results. 🔎Simple Linear Regression Formulas: Detailed application of the formulas without relying on libraries. 🔎Explanations: Simple and brief explanations of key terms and formulas related to Simple Linear Regression. 🔎Train-Test Split: Implementation of train-test splitting without using libraries. #linearregression #python #statisticalmodeling #statisticalanalysis #beginnerfriendly

Day 46: Cross-Validation > Stratified Cross-Validation: For classification tasks, ensure each fold maintains the same proportion of classes as in the entire dataset, improving the reliability of performance estimates. > K-Fold Cross-Validation: Split the data into K subsets (folds). Train on K−1 folds and validate on the remaining fold, repeating K times. This helps get a more robust estimate of model performance. #PythonForDataScience #DailyChallenge #DataScience #LearningCommunity #MachineLearning #DataScientist #Python #ML

🚀 I've published another blog on my Machine Learning Series! In this post, you'll know what 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 is, how it handles non-linearity, and the steps to implement it in Python. I’ve also included some visualizations to help you understand the concept better, plus a quiz at the end to test your SVR knowledge. 🔗 New blog link: https://lnkd.in/erm9JDiU 🔗 Machine Learning Blog Series link : https://lnkd.in/eRGXtEx9 Check it out and let me know what you think! 😊 #MachineLearning #DataScience #SVR #Python #Tech PS: Don't forget to stay hydrated!

Day 18 | 3D | #30DayMapChallenge 🌍🗺 Exploring the world in three dimensions is absolutely exciting! This visualization was created using MapLibre with Leafmap, bringing 3D mapping to life. 📕 Feel free to explore the notebook here: https://lnkd.in/dCduXXtv 📽 Tutorial playlist: https://lnkd.in/djJ3rqTf #Esri #Leafmap #MapLibre #GIS #Kilimanjaro #3DMapping #python

✨ Explored the RANSAC algorithm in depth because it played a crucial role in one of the image-processing projects we're working on. Later, I discovered that it can be applied across various applications with minimal changes required to the data format. ✨ So I developed the code from scratch to implement this algorithm—thanks to GPT! Feel free to check it out 😊 📌 Code: https://lnkd.in/g4AycvgN #opencv #python #ransac #imageprocessing #featurematching

🚀 Day 55 of #100DaysOfCode 📈 Today, I tackled the "Sort Array by Increasing Frequency" problem on LeetCode! This challenge involves sorting an array based on the frequency of the values and, in case of ties, sorting them in decreasing order. 💡 Key insights: Used the Counter class from the collections module to count the frequency of each number. Employed a heap (priority queue) to handle the sorting based on frequency and value. Leveraged heapq to efficiently sort and build the final result array. #LeetCode #Python #CodingChallenge #ProblemSolving #Algorithms #DataStructures #100DaysOfCode #FrequencySort #Heap