Rebuilding consistency with Simple Linear Regression basics

Day 2 of my 100 Days of Machine Learning Yesterday I explained the difference between Classification and Regression. Today I’m diving deeper into Linear Regression, one of the simplest yet most powerful ML algorithms. At its core, Linear Regression fits a line to data points by minimizing errors. (y = mx + c) Ordinary Least Squares (OLS) → the method used to find the best-fit line. Sum of Squared Residuals (SSR) → measures how far predictions are from actual values. The idea is simple: find the line where the SSR is as small as possible. #MachineLearning #LinearRegression #Statistics #DataScience #100DaysOfML

🌳 "From roots to branches, Decision Trees make learning in ML feel natural." On Day 32 of my ML journey, I started exploring Decision Trees — one of the most intuitive yet powerful algorithms. Excited to dive deeper into how they split data, reduce impurity, and make predictions step by step. 🚀 #MachineLearning #Day32 #DecisionTrees #MLJourney #100DaysOfML #LearningInPublic

Week 10 Recap on Digital Skola 🌟 | Basic Supervised Learning This week, I explored some fundamental concepts in supervised machine learning: 📌 Classification 📊 Logistic Regression 🔍 K-Nearest Neighbors (KNN) 🌳 Decision Trees 🤝 Ensemble Learning 🎯 Evaluation Metrics Each method brings a unique way of making predictions, and understanding their strengths helps in choosing the right model for different problems. Excited to keep building my ML knowledge step by step 🚀 #DigitalSkola #LearningProgressReview #DataScience

Ever feel like ML algorithms are picky eaters? They are! Each one needs its own special hyperparameter seasoning. Check out this cheat sheet: - Linear Regression likes a dash of L1/L2 Penalty and a sprinkle of Solver. - Naive Bayes? Just add Alpha and Fit Prior to taste. - Random Forest? It’s all about Max Depth and N Estimators (trees love company). Next time you’re tuning, remember: ML models are like houseplants—each one has its own care instructions. Ignore them, and things get... wilted. 🌱 #MachineLearning #DataScience #HyperparameterTuning

Ever feel like ML algorithms are picky eaters? They are! Each one needs its own special hyperparameter seasoning. Check out this cheat sheet: - Linear Regression likes a dash of L1/L2 Penalty and a sprinkle of Solver. - Naive Bayes? Just add Alpha and Fit Prior to taste. - Random Forest? It’s all about Max Depth and N Estimators (trees love company). Next time you’re tuning, remember: ML models are like houseplants—each one has its own care instructions. Ignore them, and things get... wilted. 🌱 #MachineLearning #DataScience #HyperparameterTuning

🗓 Day 32 of my #BackToFlow journey to rebuild consistency — diving deeper into Machine Learning fundamentals 🤖📊 Today’s focus: Distance of a Point from a Plane → Understanding the geometric intuition behind how we measure separation in higher dimensions (a key step for algorithms like SVM). Instance-based vs Model-based Learning → Instance-based (like k-NN): Store the data and make predictions by comparing with known examples. Model-based (like Linear Regression, SVM): Learn a mathematical model that generalizes from the data. Loving how math + intuition come together to form the backbone of ML algorithms. 🚀 #MachineLearning #DataScience #LearningJourney #BackToFlow #Consistency

🗓 Day 38 of my #BackToFlow journey to rebuild consistency— continuing with Linear Regression 📊 Today’s focus: Ordinary Least Squares (OLS) → The most common method to estimate parameters in Linear Regression. Idea: Find the line (or hyperplane) that minimizes the sum of squared errors between predicted and actual values. Explored how OLS gives the “best fit” and why it’s mathematically efficient. OLS feels like the backbone of regression — simple yet powerful in building the foundation for more advanced ML algorithms. 🚀 #MachineLearning #LinearRegression #OrdinaryLeastSquares #BackToFlow #LearningJourney #Consistency

🚀 Day 21 – My Learning & Sharing Series Today we move forward in the ML journey with one of the simplest yet powerful algorithms — K-Nearest Neighbors (KNN). 📍 🔹 KNN (K-Nearest Neighbors) A supervised learning algorithm used for both classification & regression. Works on the principle of similarity: predictions are made based on the closest data points in feature space. Easy to understand, non-parametric, and effective for smaller datasets. Sensitive to feature scaling & choice of K. 👉 Sometimes, the simplest algorithms can teach us the strongest fundamentals. 🌱 #MachineLearning #KNN #Classification #Regression #DataScience #LearningResources

Hello LinkedIn, I'd like to present my latest issue on "Efficient Algorithm Synthesis for High-Dimensional Data Clustering using AI-Driven Methods and Algebraic Techniques" In high-dimensional data clustering, traditional algorithms often struggle with scalability issues. To address this challenge, I developed an AI-driven method that leverages machine learning techniques to synthesize efficient algorithms for large-scale financial datasets. This approach enables faster and more accurate cluster analysis, which is crucial in identifying market trends and making informed investment decisions. link:https://lnkd.in/ecaq3YHQ #FinancialAnalytics #DataScience #AIinFinance #AlgebraicTechniques

I published my slides: "Regularization" on Zenodo: https://lnkd.in/dRr6vtjE The slides give a structured overview of regularization in machine learning. They begin by motivating why regularization is needed (to control overfitting, improve generalization, enforce sparsity, or impose constraints). Then they explain ridge regression (ℓ2 regularization), deriving its closed-form solution and interpreting it from both optimization and Bayesian perspectives. Next, the slides show how ℓ2 and ℓ1 regularization affect optimization processes and optimal solutions, with ℓ1 in particular inducing sparsity. Finally, they illustrate how adding noise to inputs or weights can be seen as another form of regularization, linking back to concepts like ridge regression.