#Data Preprocessing

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#Data Preprocessing Reel by @priyal.py - Preprocessing pipeline for llm #datascience #machinelearning #womeninstem #learningtogether #progresseveryday
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@priyal.py
Preprocessing pipeline for llm #datascience #machinelearning #womeninstem #learningtogether #progresseveryday
#Data Preprocessing Reel by @chrisoh.zip - The best projects serve a real use case Comment "data" for all the links and project descriptions #tech #data #datascience #ml #explore
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@chrisoh.zip
The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore
#Data Preprocessing Reel by @lindavivah (verified account) - Let's see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn't just training a model. A production ML lifecycle typically looks li
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@lindavivah
Let’s see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn’t just training a model. A production ML lifecycle typically looks like this: 1️⃣ Define the problem & objective 2️⃣ Collect and (if needed) label data 3️⃣ Split into train / validation / test sets 4️⃣ Data preprocessing & feature engineering 5️⃣ Train the model (forward pass + backpropagation in deep learning) 6️⃣ Evaluate on held-out data to measure generalization 7️⃣ Hyperparameter tuning (learning rate, architecture, etc.) 8️⃣ Final testing before release 9️⃣ Deploy (batch inference or real-time serving behind an API) 🔟 Monitor for data drift, concept drift, latency, cost, and reliability 1️⃣1️⃣ Retrain when performance degrades Training updates weights. Evaluation measures performance. Deployment serves predictions. Monitoring keeps the system healthy. It’s not linear. It’s a loop. And once you move beyond a single experiment, that loop becomes a systems problem. At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle. Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
#Data Preprocessing Reel by @thephdstudent (verified account) - Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to
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@thephdstudent
Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to move from spreadsheets to web-based visualisations ✨ Covers tools like Google Sheets, Datawrapper, Tableau Public, Chart.js & Leaflet ✨ Perfect if you want to communicate data clearly — even without heavy coding ✨Open-source so freely available online 📌 Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code — Jack Dougherty & Ilya Ilyankou 💭 Summary: This book shows you how to clean, analyse, and visualise data using practical tools — starting with spreadsheets and moving into customisable web-based charts and maps. It’s especially useful if you want to share your work online and make your data interactive, not just informative. If you’re learning data science, bioinformatics, or just want to present your work better, this is a great place to start 🤍 📌 Save this for later — I’ll be sharing more recommendations soon. #womeninstem #datavisualization #datascience #bioinformatics #tech
#Data Preprocessing Reel by @data_with_anurag (verified account) - 🚨 Want to become a Data Analyst but don't know where to start? 👀 I've got you covered - Microsoft has launched a dedicated learning path with free
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@data_with_anurag
🚨 Want to become a Data Analyst but don’t know where to start? 👀 I’ve got you covered — Microsoft has launched a dedicated learning path with free resources to help you master Data Analytics step by step! 📊 💬 Comment “DATA” and I’ll DM you the complete roadmap + official Microsoft resources. ✅ Beginner to advanced topics covered ✅ 100% FREE learning materials ✅ Certificate-ready path to build your career 🔥 This is your sign to start learning data analytics the right way — straight from Microsoft! 🚀
#Data Preprocessing Reel by @datasciencebrain (verified account) - The only Data Science & AI cheat sheet you'll ever need 🔥 ⬇️ Want the full PDF cheat sheet for FREE? Comment "CHEAT" below 👇 300+ functions. 8 lib
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@datasciencebrain
The only Data Science & AI cheat sheet you'll ever need 🔥 ⬇️ Want the full PDF cheat sheet for FREE? Comment "CHEAT" below 👇 300+ functions. 8 libraries. Real code examples. 🐼 Pandas — 70+ functions with examples 🔢 NumPy — Array ops, linear algebra & more 🗄️ SQL — Joins, window functions, CTEs 📊 Excel — XLOOKUP, dynamic arrays, LAMBDA 📈 Matplotlib — Every chart type covered 🤖 Scikit-Learn — Full ML pipeline in one sheet 🔥 PyTorch — Tensors to training loops 🦜 LangGraph — Agents, memory, HITL & tools This is the resource I wish I had when I started 📌Save this post, you WILL need it later 📲 Follow @datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆 . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering
#Data Preprocessing Reel by @she_explores_data - Python NumPy Essentials for Data Science and ML NumPy is the foundation of almost every data science and machine learning workflow. From creating eff
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@she_explores_data
Python NumPy Essentials for Data Science and ML NumPy is the foundation of almost every data science and machine learning workflow. From creating efficient arrays to performing statistical analysis and reshaping data for models, these functions are used daily by analysts, engineers, and researchers. This series covers the core NumPy operations that help you: • Build and manage arrays efficiently • Reshape and combine data for analysis • Perform statistical computations at scale • Filter, index, and clean numerical data • Store and load arrays for real-world projects Save this post for reference and revisit it whenever you work with numerical data in Python. [python,numpy,data science,machine learning,ml basics,array operations,numerical computing,data analysis,python libraries,statistics in python,data preprocessing,data manipulation,vectorization,scientific computing,python for beginners,python for data analysis,analytics tools,data engineering basics,ai foundations,ml preparation,coding for analysts,python skills,data workflows,tech careers,learning python,python ecosystem,data structures,ndarray,python arrays,statistical analysis,feature engineering,model preparation,data cleaning,python coding,developer skills,data tools,analytics career,python cheatsheet,ml tools,python learning,programming fundamentals,data skills] #Python #NumPy #DataScience #MachineLearning #DataAnalytics
#Data Preprocessing Reel by @ritzrebelsoul - From data to decisions - powered by ML 💡🤖 #machinelearning #datascience #ai #technology #datascience
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@ritzrebelsoul
From data to decisions — powered by ML 💡🤖 #machinelearning #datascience #ai #technology #datascience
#Data Preprocessing Reel by @jessramosdata (verified account) - How I built this data analytics project that uses LLMs to interpret multimodal data (KPI images)! ↳ Pulled the KPI images from a PDF ↳ Connected to
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@jessramosdata
How I built this data analytics project that uses LLMs to interpret multimodal data (KPI images)! ↳ Pulled the KPI images from a PDF ↳ Connected to OpenAI’s GPT-4 nano ↳ Input prompt & images into LLM ↳ Wrote the results to a report PDF (and I did it without any code too 😉) I used @knimesoftware to put together all the steps visually and create this analysis! It’s free and open source, so you can do it too! KNIME owns the entire data analytics pipeline and makes it really easy to follow the logic from raw data to final output. And it has so many data connections so you can pull data from spreadsheets, databases, APIs, cloud services, and more! Try KNIME today! #data #dataanalytics #knime #project #ai
#Data Preprocessing Reel by @prashant.code - Data Normalization in ML ⬇️ Ever wondered why some features overpower others in ML models? 🤔 It's all about scaling! Today, I demonstrated how to u
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@prashant.code
Data Normalization in ML ⬇️ Ever wondered why some features overpower others in ML models? 🤔 It’s all about scaling! Today, I demonstrated how to use Min-Max Scaling to normalize data and bring all features into the same range. 💻 Code Highlight: from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() normalized_data = scaler.fit_transform(data) 🎯 Why Normalize? ➡️ Prevents larger features from dominating. ➡️ Speeds up model convergence. ➡️ Essential for distance-based algorithms like KNN or SVM. 💬 Do you normalize your data before training ML models? Let me know below! Follow for more @prashant.code ♥️ #education #datascience #ai #machinelearning #trending #normalization #minmaxscaling #datapreprocessing #python #mltips #coding #prashantcode #reelcontent
#Data Preprocessing Reel by @loresowhat (verified account) - Comment "CODE" and I will send you the full code! 🧹 Tired of messy customer data ruining your analysis? Here's how to build a complete data cleani
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@loresowhat
Comment “CODE” and I will send you the full code! 🧹 Tired of messy customer data ruining your analysis? Here’s how to build a complete data cleaning pipeline in SQL that transforms chaos into crystal-clear insights! 💡 Example: You have customer records with mixed case names, inconsistent phone formats, duplicate emails, and missing values. Instead of manual cleanup, use SQL to automate the entire process and get analysis-ready data in minutes. Stop wasting hours on manual data cleanup. Build this pipeline once and transform any messy dataset into gold. 👉 FOLLOW @loresowhat for more practical data analytics tips 🚀 #dataanalytics #dataanalysis #sql #datacleaning #datapipeline

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