288.1K
PRPreprocessing pipeline for llm
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday
@priyal.py
#Data Preprocessing
世界中の人々によるData Preprocessingに関する件のリール動画を視聴。
ログインせずに匿名で視聴。
関連検索
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トレンドリール
(12)
433.4K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
43.6K
LILet’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! 🤍
@lindavivah
79.0K
THData 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
@thephdstudent
155.6K
DA🚨 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_with_anurag
322.8K
DAThe 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🏆
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[dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai]
#datascience #dataanalyst #machinelearning #genai #aiengineering
@datasciencebrain
229.7K
SHPython 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
@she_explores_data
253
RIFrom data to decisions — powered by ML 💡🤖
#machinelearning
#datascience #ai #technology #datascience
@ritzrebelsoul
81.6K
JEHow 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
@jessramosdata
5.1K
DAUnderstand the Need #datascience #datavisualization #dataanalytics #dataanalyst
@data_analytics_2121
3.4K
PRData 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
@prashant.code
11.2K
LOComment “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
@loresowhat
✨ #Data Preprocessing発見ガイド
Instagramには#Data Preprocessingの下にthousands of件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
#Data Preprocessingは現在、Instagram で最も注目を集めているトレンドの1つです。このカテゴリーにはthousands of以上の投稿があり、@chrisoh.zip, @datasciencebrain and @priyal.pyのようなクリエイターがバイラルコンテンツでリードしています。Pictameでこれらの人気動画を匿名で閲覧できます。
#Data Preprocessingで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
📹 ビデオトレンド: 最新のReelsとバイラル動画を発見
📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索
🌟 注目のクリエイター: @chrisoh.zip, @datasciencebrain, @priyal.pyなどがコミュニティをリード
#Data Preprocessingについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Data Preprocessingのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
パフォーマンス分析
12リールの分析
💡 トップ投稿は平均318.5K回の再生（平均の2.3倍）
ピーク時間（11-13時、19-21時）とトレンド形式に注目
コンテンツ作成のヒントと戦略
💡 トップコンテンツは10K以上再生回数を獲得 - 最初の3秒に集中
✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長651文字
📹 #Data Preprocessingには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用
✨ 多くの認証済みクリエイターが活動中（50%） - コンテンツスタイルを研究
#Data Preprocessing に関連する人気検索
🎬動画愛好家向け
Data Preprocessing ReelsData Preprocessing動画を見る
📈戦略探求者向け
Data Preprocessingトレンドハッシュタグ最高のData Preprocessingハッシュタグ
🌟もっと探索
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