Learning K-Nearest Neighbors (KNN) for ML

🗓 Day 34 of my #BackToFlow journey to rebuild consistency — back to Machine Learning basics with Simple Linear Regression 📈 Today’s focus: Introduction to Simple Linear Regression → One of the simplest yet most powerful ML algorithms that models the relationship between two variables. Understanding the Equation → Form: y = mx + c Where m = slope (how much y changes with x), and c = intercept (where the line crosses y-axis). Explored how this line is used to predict outcomes and minimize error between predicted and actual values. Even though it’s a beginner-friendly algorithm, it’s the foundation for more complex regression and ML models. 🚀 #MachineLearning #LinearRegression #DataScience #LearningJourney #BackToFlow #Consistency

Day 7 ML : Week 1 Recap: Building the Foundation This week, we covered: What machine learning is (pattern recognition at scale) Supervised vs. unsupervised learning The machine learning pipeline basics Simple algorithm breakdowns Why data quality is critical Feature engineering fundamentals Now for a quick mini‑quiz: Which of these is an example of machine learning in action? A) Identifying spam emails B) Adjusting your home thermostat manually C) Recommending movies on Netflix D) Splitting groceries evenly among friends #MachineLearning #DataScience #AITutorial #MLForBeginners #LearnAI #MLPipeline #TechEducation #AIExplained #PatternRecognition #FutureOfAI

When I first started learning machine learning, I was always looking for practical projects that explained both the code and the thought process behind it. That’s why I created this beginner-friendly repo: "𝑻𝒊𝒕𝒂𝒏𝒊𝒄 𝑳𝒐𝒈𝒊𝒔𝒕𝒊𝒄 𝑹𝒆𝒈𝒓𝒆𝒔𝒔𝒊𝒐𝒏 𝑳𝒆𝒄𝒕𝒖𝒓𝒆" In this project, I walk step by step through: - 𝗛𝗼𝘄 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮. - 𝗛𝗼𝘄 𝘁𝗼 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗶𝗰𝗵 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗮𝗻𝗱 𝘄𝗵𝘆. - 𝗛𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗼𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮𝘀𝗲𝘁. - 𝗛𝗼𝘄 𝘁𝗼 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. My goal was to show not just the mechanics of machine learning, but also how to behave with data when you approach a problem for the first time. This is not a deep or advanced project, it is written in a teaching style for 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 who want a practical starting point in ML. You can find the repo here: https://lnkd.in/djzAjGVb #MachineLearning #LogisticRegression #MLProjects #FeatureSelection #DataPreprocessing #ModelEvaluation #TeachingMachineLearning #PracticalML

🗓 Day 31 of my #BackToFlow journey to rebuild consistency— stepping into the world of Machine Learning 🤖 Today’s focus: Types of ML Techniques → Supervised, Unsupervised, and Reinforcement Learning. Equation of a Line → Revisiting the basics (y = mx + c) to understand decision boundaries. 3D Visualization → Extending linear equations into 3D space for multiple features. Hyperplane → The foundation of separating classes in higher dimensions (key for algorithms like SVM). It feels great to finally move from statistics & feature engineering into the core ML concepts. This is where the math meets real-world problem-solving. 🚀 #MachineLearning #DataScience #BackToFlow #LearningJourney #Consistency

🚀 Day 22 – My Learning & Sharing Series Today’s focus is on one of the most widely used and mathematically elegant ML algorithms — Support Vector Machines (SVM). ⚡ 🔹 SVM (Support Vector Machine) A supervised learning algorithm for classification and regression tasks. Works by finding the optimal hyperplane that best separates data into classes. Uses support vectors (critical data points) to define boundaries. Can handle linear & non-linear data through kernel tricks (Polynomial, RBF, etc.). Known for high accuracy in high-dimensional spaces, though computationally intensive for very large datasets. 👉 SVM is a great balance of theory & practicality — a must-know for every data science learner! 🌱 #MachineLearning #SVM #SupportVectorMachine #Classification #Regression #DataScience #LearningResources

Mastering Machine Learning-One Visual at a Time Just shared this structured visual roadmap of Machine Learning that helped me connect the dots across algorithms, learning types, and real-world applications. From classic models like Linear Regression and K-Means to advanced techniques like Transformers, GANs, and Q-Learning, this diagram lays out the ML universe in one glance.

K-Means Clustering is an unsupervised machine learning algorithm that helps group data points into clusters based on their inherent similarity. In this work I have shown how this algorithm works with simple practical as well. Hope you enjoy with it. 😊 #KMeans #MachineLearning #DataScience

🌟 𝐅𝐫𝐨𝐦 𝐓𝐡𝐞𝐨𝐫𝐲 𝐭𝐨 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞: 𝐂𝐍𝐍 𝐏𝐫𝐨𝐣𝐞𝐜𝐭! 🌟 I’ve been exploring 𝐂𝐨𝐧𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐂𝐍𝐍𝐬), moving from understanding the basics to implementing them in real projects." To test my learning, I built a classification model on the CIFAR-10 dataset 🖼️. 🔹 𝐏𝐡𝐚𝐬𝐞 1 – 𝐁𝐚𝐬𝐞𝐥𝐢𝐧𝐞 𝐌𝐨𝐝𝐞𝐥 (𝐍𝐨 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧) ✔ I trained a simple CNN using PyTorch. The model achieved 72.7% accuracy on the test set. 🔹𝐏𝐡𝐚𝐬𝐞 2 – 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 I then introduced some data augmentation techniques like: ✔ Random flips ✔ Random rotations ✔ Color jitter The result? A slight boost in performance ✅. ✨ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: • CNNs are powerful feature extractors for image data. • Data augmentation, even with simple techniques, can help models generalize better. • Small improvements matter when building reliable computer vision systems. This was a rewarding step in my deep learning journey. Next, I plan to explore advanced architectures like ResNet and benchmark them against my custom CNN. Always excited to learn, experiment, and share! 🚀 #DeepLearning #PyTorch #ComputerVision #MachineLearning #CNN #CIFAR10

🚀 Learning Update: Mastered K-Nearest Neighbors (KNN)! Over the past few days, I’ve been diving deep into one of the simplest yet powerful machine learning algorithms — K-Nearest Neighbors (KNN). It’s been exciting to see how such an intuitive approach can be applied to both classification and regression problems. 🔹 Classification: I explored how KNN predicts the class of a new data point by looking at the majority class of its nearest neighbors. It was great to see how the choice of k (number of neighbors) directly impacts the performance — too small k risks overfitting, while too large k may underfit. 🔹 Regression: I also implemented KNN for regression, where predictions are based on the average values of the nearest neighbors. This gave me hands-on insights into performance metrics like R² score, MAE, and MSE, which are more suitable than accuracy in regression. 🔹 Distance Metrics & Search Algorithms: I learned how KNN uses different distance metrics like Euclidean and Manhattan, and how performance can be optimized with Ball Tree and KD Tree for faster neighbor searches. Finally, I applied GridSearchCV to systematically tune hyperparameters (like k) and achieve better results. 💡 Key takeaway: KNN is simple to understand and implement, yet highly effective for many problems when tuned properly. Excited to move forward and continue my ML journey with more advanced algorithms! #MachineLearning #KNN #DataScience #LearningJourney

Today I paused to revise everything I’ve learned about Supervised Learning so far — and practiced with some questions ✅ 🔁 What I revised: Regression: Linear, Polynomial, Ridge, Lasso, Logistic Classification: Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM) Other Classifiers: KNN, Naive Bayes, SVM Metrics: RMSE, MAE, R², Accuracy, Precision, Recall, F1, ROC-AUC 📝 Practice Questions I worked on: 1️⃣ Predicting house prices → Linear Regression vs Polynomial Regression 2️⃣ Classifying Titanic survival → Decision Tree vs Random Forest 3️⃣ Spam detection with text → Naive Bayes 4️⃣ Comparing KNN, SVM, Logistic Regression on Iris dataset 5️⃣ Evaluating with Accuracy, Precision, Recall, F1 💡 Big Takeaway: Understanding algorithms is great — but choosing the right model + right metric for the problem is the real skill. 🔥 Mini Challenge for you: Imagine you’re detecting fraud in transactions. 👉 Which would you choose: Accuracy, Precision, Recall, or F1-score? #MachineLearning #SupervisedLearning #Regression #Classification #DataScience #LearningInPublic