#Machine Learning Clustering Techniques

K Means clustering algorithm in Machine Learning #machinelearning #datascience #mlengineer #deeplearning #ai #artificialintelligence #learnai #cactusai #techreels #university #exam #exams

Comment KNIME to get a link to try out this free tool! For the last 40 days I have been tracking how many hours I sleep, work, scroll, and am social. I used sleeping tracker for sleep data, work time sheets for clients, screen time for scrolling and estimating the time out. And using k-means clustering, it grouped hours vs sleep and work into 3 distinct groups. Although the data doesn’t directly label a category, you can look at each cluster and further infer what’s going on. For example, from my result it is very clear that even though sometimes I don’t believe it, when my sleep suffers, my work hours definitely does too. It’s a common pattern in the data. Using this tool, you can take a look at all different kinds of data. Personal data, sales data, gaming stats and find patterns you didn’t know exist. #datascience #clustering #learntocode #KNIME #KNIMECollab

Visualizing K-Means Clustering in real-time. Watch how the algorithm iteratively finds structure in random noise. ✖️ Centroids (X): The moving targets calculating the average position. ⚫ Points (Dots): Raw data snapping to the nearest cluster. #DataScience #MachineLearning #KMeans #Visualization #ReactJS #Algorithms #GenerativeArt #Dev #Codin

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🤯 Can a computer learn WITHOUT being given answers? Yes and it's more powerful than you think! 👇 Comment "Day 4" 👇 if you're following the series! 💡 Interview Answer Memorize this: "Unsupervised Learning deals with unlabeled data — the model finds hidden patterns on its own without being told the correct answers." One line. Maximum impact. ✅ 🧠 Simplest way to understand it: Imagine you have a bunch of coins with no labels. What do you do? You group them by size and color! 🪙 That's exactly what an ML model does finds patterns and groups data automatically! 🎬 You already use it every day — Netflix! 🍿 👉 Netflix watches what YOU watch 👉 Groups you with 1000 other users who have similar taste 👉 That's why your homepage feels personally curated! That's Unsupervised Learning in action! 🔥 🤔 Tell me in the comments: Which do you find easier to understand Supervised or Unsupervised Learning? 👇 Drop "S" for Supervised or "U" for Unsupervised! 📌 Save this to your ML revision playlist! 👥 Share this with a friend learning AI/ML! 🔔 Follow @mr.aiverse Day 5 drops tomorrow, don't miss it! 🚀 [unsupervised learning, machine learning, ML types, clustering, supervised vs unsupervised, AI, artificial intelligence, Netflix recommendation, series, 30 days of ML, trending, tech series, India, Hyderabad, Telangana, interview prep, data science, hidden patterns, mr.aiverse, day 4, reels, tech education] #UnsupervisedLearning #MachineLearning #MLTypes #Clustering #30DaysOfMachineLearning AI ArtificialIntelligence DataScience InterviewPrep NetflixML TechSeries Trending TrendingReels IndianTechCreator India Hyderabad Telangana HyderabadTech TelanganaTech AIInTelugu TechInTelugu LearnAI MrAiverse Day4 SupervisedVsUnsupervised MLBasics TechEducation ReelsViral InstagramReels AIRevolution

🤖K-means clustering is an unsupervised learning method dividing data into clusters based on similarities. It iteratively assigns data points to the nearest cluster centroid, updating centroids until convergence. 💡It aims to minimize within-cluster variance for compact, distinct clusters but is sensitive to initial centroids and can converge to local optima. 🔥Head to our link in bio to get 29% OFF on my latest generative AI course, where you can learn Generative AI (GenAI), the OpenAI API, and the ChatGPT API! It expires in 28 days, so claim it while you can 🫡 #datascience #data #datanalytics #mathematics #deeplearning #machinelearning #ai #artificialintelligence #statistics

AI / ML Basics: K Means K-Means is an unsupervised clustering algorithm that groups data points into K clusters by iteratively assigning them to the nearest centroid and updating centroids based on the mean of assigned points. If you’re using python, you can just import it from sklearn. If you want to implement it yourself (which I recommend), GeeksForGeeks has a good breakdown. I will post the implementation and simulation links on my story, follow so you don’t miss the next one! Simulation here is from https://www.naftaliharris.com/blog/visualizing-k-means-clustering/ #coding #machinelearning #csmajors #programming

K-Means: The ultimate matchmaking algorithm – grouping data points like a pro, but always asking, 'Are we really in the right cluster? #datascience #machinelearning #ai #ml #statistics #clustering

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Machine Learning Algorithms #artificialintelligence #data #algorithms #ai #ml

📍Machine learning algorithms every data scientist must know👇 1. Linear Regression: Used for predicting a continuous value. It’s simple yet effective for various problems. 2. Logistic Regression: Despite its name, it’s used for classification tasks, particularly binary classification. And I also use class probabilities (class proba), which is the probability of the class label. 3. Decision Trees: Used for both classification and regression tasks. They split data into branches to form a tree structure. 4. Gradient Boosting Machines (GBM): An ensemble technique that builds predictive models in a stage-wise fashion, often yielding high-quality predictions. I use these frequently for high accuracy and performance. 5. Random Forests: An ensemble method that uses a collection of decision trees to improve prediction accuracy and avoid overfitting. 6. Support Vector Machines (SVM): Primarily used for classification tasks, SVMs are effective in high-dimensional spaces. 7. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression. 8. Naive Bayes: A group of simple, probabilistic classifiers based on applying Bayes’ theorem with strong independence assumptions. 9. Neural Networks: Versatile and powerful, used for a wide range of tasks including classification, regression, and unsupervised learning. Deep learning models, a subset of neural networks, are particularly notable for their performance in complex tasks like image and speech recognition. Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code