#Data Science Platform

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Trend Reels

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#Data Science Platform Reels - @askdatadawn (onaylı hesap) tarafından paylaşılan video - Let's build a linear regression model together! This looks like a super simple piece of code. But that's all you need to build your first model! Of
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@askdatadawn
Let’s build a linear regression model together! This looks like a super simple piece of code. But that’s all you need to build your first model! Of course, building a production model gets more complex and requires *a few* more steps. But understanding the fundamentals is a good place to start. 🔖 Save this for later #datascience #machinelearning
#Data Science Platform Reels - @sujar.tech (onaylı hesap) tarafından paylaşılan video - These are 3 awesome machine learning projects that you can code in a weekend, and you will benefit greatly when completing them Comment "Project" for
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@sujar.tech
These are 3 awesome machine learning projects that you can code in a weekend, and you will benefit greatly when completing them Comment “Project” for the full list of projects so you can start building these this weekend… It gives you a good overview of Neural Networks, PyTorch,Python, SpaCy(NLP),Preprocessing,Convolutional Neural Networks,Classifiers, Website Building(if you do the complex routes),Datasets,Training and Testing, and many more topics… #coding #computerscience #ml #machinelearning
#Data Science Platform Reels - @dailydoseofds_ tarafından paylaşılan video - Time complexity of 10 ML algorithms 📊 (must-know but few people know them) Understanding the run time of ML algorithms is important because it help
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@dailydoseofds_
Time complexity of 10 ML algorithms 📊 (must-know but few people know them) Understanding the run time of ML algorithms is important because it helps us: → Build a core understanding of an algorithm → Understand the data-specific conditions that allow us to use an algorithm For instance, using SVM or t-SNE on large datasets is infeasible because of their polynomial relation with data size. Similarly, using OLS on a high-dimensional dataset makes no sense because its run-time grows cubically with total features. Check the visual for all 10 algorithms and their complexities. 👉 Over to you: Can you tell the inference run-time of KMeans Clustering? #machinelearning #datascience #algorithms
#Data Science Platform Reels - @smart_skale_ tarafından paylaşılan video - Models change. Data changes. Results change. If you don't track versions, you can't track performance. Model Versioning = Control + Reproducibility +
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@smart_skale_
Models change. Data changes. Results change. If you don’t track versions, you can’t track performance. Model Versioning = Control + Reproducibility + Safe Rollbacks @smart_skale_ #MachineLearning #ModelVersioning #MLOps #DataScience #AI
#Data Science Platform Reels - @codingwithmee_18 tarafından paylaşılan video - 🚀 Machine Learning Workflow: Step-by-Step Breakdown Understanding the ML pipeline is essential to build scalable, production-grade models. 👉 Initia
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@codingwithmee_18
🚀 Machine Learning Workflow: Step-by-Step Breakdown Understanding the ML pipeline is essential to build scalable, production-grade models. 👉 Initial Dataset Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features. Example: Drop constant features or remove columns with 90% missing values. 👉 Exploratory Data Analysis (EDA) Use mean, median, standard deviation, correlation, and missing value checks. Techniques like PCA and LDA help with dimensionality reduction. Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance. 👉 Input Variables Structured table with features like ID, Age, Income, Loan Status, etc. Ensure numeric encoding and feature engineering are complete before training. 👉 Processed Dataset Split the data into training (70%) and testing (30%) sets. Example: Stratified sampling ensures target distribution consistency. 👉 Learning Algorithms Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting. Example: Use Random Forest to capture non-linear interactions in tabular data. 👉 Hyperparameter Optimization Tune parameters using Grid Search or Random Search for better performance. Example: Optimize max_depth and n_estimators in Gradient Boosting. 👉 Feature Selection Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features. Example: Drop features with zero importance to reduce overfitting. 👉 Model Training and Validation Use cross-validation to evaluate generalization. Train final model on full training set. Example: 5-fold cross-validation for reliable performance metrics. 👉 Model Evaluation Use task-specific metrics: - Classification – MCC, Sensitivity, Specificity, Accuracy - Regression – RMSE, R², MSE Example: For imbalanced classes, prefer MCC over simple accuracy. 💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications. @codingwithmee_18 follow for more #viral#explore#trending#trendy#explorepage
#Data Science Platform Reels - @nomidlofficial tarafından paylaşılan video - 🧠 Start your week by strengthening your data science fundamentals. Part 2 covers concepts that directly impact how models learn and perform: • Bagg
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@nomidlofficial
🧠 Start your week by strengthening your data science fundamentals. Part 2 covers concepts that directly impact how models learn and perform: • Bagging vs Boosting in ensemble learning • Entropy & Information Gain in decision trees • Precision vs Recall for model evaluation Mastering these ideas helps you build smarter and more reliable ML models. 📌 Save this for later 🔁 Share with a Python/ML learner 📌 Tap the link in @nomidlofficial’s bio 🔗 Read more info: https://www.nomidl.com/machine-learning/3-concepts-every-data-scientist-must-know-part-2/ #DataScience #MachineLearning #AI #DeepLearning #LearnML
#Data Science Platform Reels - @samarthtuliofficial (onaylı hesap) tarafından paylaşılan video - Share with or tag someone who needs to see this! Most ML conversations stop at algorithms. In practice, ML is about constraints - not complexity. •
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@samarthtuliofficial
Share with or tag someone who needs to see this! Most ML conversations stop at algorithms. In practice, ML is about constraints — not complexity. • Limited data • Need for interpretability • Latency or cost limits • Constant data drift The “best” model often loses to the one that’s simpler, cheaper, and easier to maintain. Real impact happens when you think beyond models — and design systems: pipelines, feedback loops, monitoring. That’s how ML moves from experiments to production. #ml #ai #data #machinelearning #datascience
#Data Science Platform Reels - @vidyanex_consulting tarafından paylaşılan video - Most people learn Machine Learning… but get stuck when it comes to practice. DSA has LeetCode. ML deserves the same. If you're serious about AI, ML, a
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@vidyanex_consulting
Most people learn Machine Learning… but get stuck when it comes to practice. DSA has LeetCode. ML deserves the same. If you’re serious about AI, ML, and real-world skills, this is for you. 💬 Comment ML or DM AI to get the website 📌 Save this for later 🚀 Follow for more ML & AI practice resources #MachineLearning #AIPractice #DataScience #MLJourney #VidyaNex
#Data Science Platform Reels - @nomidlofficial tarafından paylaşılan video - Data Science isn't just about models - it's about understanding the core concepts behind them. Here are 3 essential concepts every data scientist mus
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@nomidlofficial
Data Science isn’t just about models — it’s about understanding the core concepts behind them. Here are 3 essential concepts every data scientist must master 👇 ✅ Sampling techniques for handling large datasets ✅ Type 1 & Type 2 Errors (False Positives vs False Negatives) ✅ Normalization vs Standardization in ML models Mastering these basics helps you build more accurate and reliable machine learning systems. 📖 Read more info: https://www.nomidl.com/machine-learning/3-concepts-every-data-scientist-must-know-part-3/ 📌 Save this for later 🔁 Share with a Python/ML learner 📌 Tap the link in @nomidlofficial’s bio #DataScience #MachineLearning #AICommunity #PythonLearning #MLConcepts
#Data Science Platform Reels - @smart_skale_ tarafından paylaşılan video - Your model was perfect last year… But today it's failing. That's not a bug. That's Model Drift. Data changes. User behavior changes. Your model must a
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@smart_skale_
Your model was perfect last year… But today it’s failing. That’s not a bug. That’s Model Drift. Data changes. User behavior changes. Your model must adapt. @smart_skale_ #MachineLearning #ModelDrift #MLOps #DataScience #AI
#Data Science Platform Reels - @berk.py (onaylı hesap) tarafından paylaşılan video - Comment "LINK" to get links! 🚀 Want to learn Machine Learning in a way that actually sticks? This beginner friendly roadmap helps you go from zero k
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@berk.py
Comment "LINK" to get links! 🚀 Want to learn Machine Learning in a way that actually sticks? This beginner friendly roadmap helps you go from zero knowledge to understanding real world machine learning, artificial intelligence, and data science concepts step by step. 🎓 Learn Machine Learning Like a Genius Perfect starting point if you feel overwhelmed by AI and machine learning. You will learn how to study machine learning efficiently, what topics to focus on first, and how to avoid wasting time while building strong fundamentals in Python, math, and algorithms. 📘 The Complete Machine Learning Roadmap Now deepen your knowledge. This resource explains supervised learning, unsupervised learning, neural networks, deep learning basics, model training, and evaluation. It gives you a clear path to become confident in data science and AI development. 💻 Machine Learning Explained in 100 Seconds Time to simplify everything. This quick overview reinforces the core ideas behind machine learning and artificial intelligence so you clearly understand how models learn from data and make predictions. 💡 With these Machine Learning resources you will: Understand core machine learning and AI concepts Learn the roadmap to become a data scientist or ML engineer Build strong foundations in algorithms and model training Prepare for tech interviews in AI and data science roles If you are serious about artificial intelligence, data science, or becoming a machine learning engineer, this roadmap will give you clarity and direction. 📌 Save this post so you do not lose the roadmap. 💬 Comment "LINK" and I will send you all the links. 👉 Follow for more content on AI, machine learning, and software engineering.
#Data Science Platform Reels - @datavisionhub tarafından paylaşılan video - Most ML projects fail because of bad data, not bad models ❌🤖 Common mistakes: ⚠️ No dataset versioning ⚠️ Silent data changes ⚠️ Wrong business logs
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@datavisionhub
Most ML projects fail because of bad data, not bad models ❌🤖 Common mistakes: ⚠️ No dataset versioning ⚠️ Silent data changes ⚠️ Wrong business logs Result? Unstable models 📉 Wrong predictions 😬 Lost trust 🚫 Lesson: Strong Data = Strong AI 💪✨ Keep learning. Keep building. 🚀 #DataScienceLife #MachineLearning #AIEngineer #MLOps

✨ #Data Science Platform Keşif Rehberi

Instagram'da #Data Science Platform etiketi altında thousands of paylaşım bulunuyor ve platformun en canlı görsel ekosistemlerinden birini oluşturuyor. Bu devasa koleksiyon, şu an gerçekleşen trend anları, yaratıcı ifadeleri ve küresel sohbetleri temsil ediyor.

#Data Science Platform etiketi, Instagram dünyasında şu an en çok ilgi gören akımlardan biri. Toplamda thousands of üzerinde paylaşımın bulunduğu bu kategoride, özellikle @askdatadawn, @sujar.tech and @berk.py gibi üreticilerin videoları ön plana çıkıyor. Pictame ile bu popüler içerikleri anonim olarak izleyebilirsiniz.

#Data Science Platform dünyasında neler viral? En çok izlenen Reels videoları ve viral içerikler yukarıda yer alıyor. Yaratıcı hikaye anlatımını, popüler anları ve dünya çapında milyonlarca görüntüleme alan içerikleri keşfetmek için galeriyi inceleyin.

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🌟 Öne Çıkanlar: @askdatadawn, @sujar.tech, @berk.py ve diğerleri topluluğa yön veriyor

#Data Science Platform Hakkında SSS

Pictame ile Instagram'a giriş yapmadan tüm #Data Science Platform reels ve videolarını izleyebilirsiniz. Hesap gerekmez ve aktiviteniz gizli kalır.

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✅ Orta Seviye Rekabet

💡 En iyi performans gösteren içerikler ortalama 2.7K görüntüleme alıyor (ortalamadan 2.4x fazla). Orta seviye rekabet - düzenli paylaşım momentum oluşturur.

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📹 #Data Science Platform için yüksek kaliteli dikey videolar (9:16) en iyi performansı gösteriyor - iyi aydınlatma ve net ses kullanın

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