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#Machinelearning Reel by @mar_antaya (verified account) - Do you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
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@mar_antaya
Do you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
#Machinelearning Reel by @aibutsimple - K-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to e
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AI
@aibutsimple
K-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other. They can be used for classification and regression. Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let’s say you get a new data point you haven’t seen before, and want to know if it should be red or blue. KNN looks at the “K” closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points. If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It’s like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity. Want to get better at machine learning? Accelerate your ML learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: visually explained Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn
#Machinelearning Reel by @equationsinmotion - The Secret Behind Every Trend Line ! #LeastSquares #LinearRegression #DataScience #Math #Statistics #MachineLearning Ever wondered how software finds
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@equationsinmotion
The Secret Behind Every Trend Line ! #LeastSquares #LinearRegression #DataScience #Math #Statistics #MachineLearning Ever wondered how software finds the perfect line through messy data points? This short animation explains the Least Squares Method, the backbone of linear regression. We visualize the difference between data points and the trend line as physical squares, showing exactly what it means to minimize the sum of squared errors. Watch as the line adjusts its slope and intercept until it finds the optimal fit for the data set.
#Machinelearning Reel by @illariy.ai - k-Nearest Neighbors 📍 #ia #machinelearning #ciencia #knearestneighbors #math
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@illariy.ai
k-Nearest Neighbors 📍 #ia #machinelearning #ciencia #knearestneighbors #math
#Machinelearning Reel by @smith.iscoding - Learning something new everyday 📚 . . . . . . . #softwareengineer #codingisfun #growthmindset #machinelearning #sydneyengineers #algorithms #deeplear
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@smith.iscoding
Learning something new everyday 📚 . . . . . . . #softwareengineer #codingisfun #growthmindset #machinelearning #sydneyengineers #algorithms #deeplearning
#Machinelearning Reel by @realbigbrainai - Python wasn't built to be trendy - it was built to be useful. Guido van Rossum created Python because C was powerful but unsafe, and shell scripts we
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@realbigbrainai
Python wasn't built to be trendy - it was built to be useful. Guido van Rossum created Python because C was powerful but unsafe, and shell scripts were too limited in scope. He wanted a language that was easier to use, safer than C, and smart enough to handle things like memory management and bounds checking - without slowing developers down. That decision is why Python powers Al, machine learning, data science, automation, and startups worldwide today. Sometimes the best tech isn't invented to impress it's invented to solve a real problem. Do you think Python is still the best beginner-friendly language in 2026? Follow @realbigbrainai to stay up to date with the latest Al news.
#Machinelearning Reel by @nardosedaily - 💻 Tech & AI (Top Earners) 1. AI / Machine Learning Engineer - $150K-$400K+ 2. Data Scientist - $130K-$250K 3. Cybersecurity Engineer - $140K-$300K
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@nardosedaily
💻 Tech & AI (Top Earners) 1. AI / Machine Learning Engineer – $150K–$400K+ 2. Data Scientist – $130K–$250K 3. Cybersecurity Engineer – $140K–$300K 4. Cloud Architect – $160K–$350K 5. Blockchain Developer – $150K–$300K 🩺 Medical & Health (Recession-Proof Money) 6. Surgeon – $350K–$700K+ 7. Anesthesiologist – $350K–$550K 8. Psychiatrist – $250K–$500K 9. Dentist / Orthodontist – $200K–$450K 10. Travel Nurse / Nurse Anesthetist (CRNA) – $180K–$350K ⚖️ Law, Finance & Corporate Power Roles 11. Corporate Lawyer – $200K–$500K+ 12. Investment Banker – $200K–$600K 13. Hedge Fund Manager – $500K–$Millions 14. Private Equity Partner – $500K–$Millions 15. CEO / C-Suite Executive – $300K–$Millions 🏗️ High-Skill Trades & Engineering (Quiet Money) 16. Petroleum Engineer – $180K–$300K 17. Construction Project Manager – $150K–$250K 18. Electrical Engineer (Specialized) – $140K–$220K 19. Air Traffic Controller – $150K–$250K 20. Elevator Technician / Union Trades – $120K–$200K+
#Machinelearning Reel by @datasciencebrain (verified account) - Main Challenges in Machine Learning: 1. Insufficient or Poor-Quality Data Lack of labeled data for supervised learning. Noisy, incomplete, or biase
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@datasciencebrain
Main Challenges in Machine Learning: 1. Insufficient or Poor-Quality Data Lack of labeled data for supervised learning. Noisy, incomplete, or biased data can lead to poor models. 2. Overfitting and Underfitting Overfitting: Model performs well on training data but poorly on new data. Underfitting: Model is too simple to capture the underlying pattern. 3. High Computational Cost Training complex models (e.g., deep learning) requires powerful hardware and GPUs. 4. Scalability Models trained on small datasets may not scale well to real-world data. 5. Model Interpretability Many powerful models (like deep neural networks) act as "black boxes" with low transparency. 6. Data Privacy and Security Legal and ethical concerns around collecting and using personal data (e.g., GDPR). 7. Bias and Fairness Models can inherit or amplify biases present in training data, leading to unfair outcomes. 8. Deployment and Maintenance Moving from prototype to production can be complex (MLOps needed). Continuous monitoring and updating are essential. 9. Choosing the Right Algorithm Selecting the most suitable model and tuning it can be time-consuming and non-trivial. 10. Domain Knowledge Understanding the domain is crucial to feature selection, data preparation, and result interpretation. Special Benefits for Our Instagram Subscribers 🔻 ➡️ Free Resume Reviews & ATS-Compatible Resume Template ➡️ Quick Responses and Support ➡️ Exclusive Q&A Sessions ➡️ Data Science Job Postings ➡️ Access to MIT + Stanford Notes ➡️ Full Data Science Masterclass PDFs ⭐️ All this for just Rs.45/month! #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp
#Machinelearning Reel by @sorhan.hq (verified account) - Researcher name: Guangting Yu, Dailey Labs The next wave of AI isn't just chat. It's simulation. As models get better at learning dynamics, differe
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@sorhan.hq
Researcher name: Guangting Yu, Dailey Labs The next wave of AI isn’t just chat. It’s simulation. As models get better at learning dynamics, differential equations, and world models, AI moves closer to running experiments, testing ideas in simulated environments, and helping engineer real systems before they’re built. Simulation engineering may be one of the biggest frontiers in AI. 🌊 #startuplife #founder #tech #machinelearning #ai
#Machinelearning Reel by @sopi.iscoding (verified account) - me every day 🥹🥲 #research #computerscience #datascience #computervision #girlwhocodes #codinglife #softwareengineer #artificialintelligence #study
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@sopi.iscoding
me every day 🥹🥲 #research #computerscience #datascience #computervision #girlwhocodes #codinglife #softwareengineer #artificialintelligence #studygram #machinelearning #womenintech #womenwhocode #tech #learningdiary #researchpaper

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