134.9K
MAAmazing websites (part 96) visit uncensored.com the #1 uncensored AI chatbot #websites #aitools
@maskedguyai
#Datascience Learning
Dünyanın dört bir yanından insanlardan Datascience Learning hakkında 86K Reels videosu izle.
Giriş yapmadan anonim olarak izle.
86K posts
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1.9M
SEPowerful websites you should know (part 827) thousands of courses in almost any field #study #learn #programming
@setupsai
192.5K
_RFree webinar😱⁉️
AI Machine learning & Data science
#rtr #rtrvlogs
@_rtr_vlogs_
472.3K
THWant to become a Machine Learning Engineer in 2025?
Build real projects that reflect how ML is done in the industry:
1 → End-to-End ML Pipeline
Predict something useful (like student dropout risk). Clean with Pandas, train with LightGBM, deploy with FastAPI + Docker + AWS.
2 → RAG Chatbot
Build a chatbot that answers from your course notes. Use LlamaIndex + FAISS + Llama 3.1. This is how GenAI apps work today.
3 → Fine-Tune LLMs
Take an open-source LLM and fine-tune it on your own dataset. Use QLoRA with PEFT. Example: medical Q&A bot.
4 → Model Monitoring
Build a fraud detection model and track drift post-deployment using Evidently AI + Weights & Biases. Shows you think beyond training.
5 → Multimodal AI App
Photo → nutrition info + recipe. Use CLIP or Florence-2 for vision-text, connect to LLaVA or Qwen-VL, deploy with Streamlit.
This stack hits every part of the ML lifecycle—from classic ML to GenAI to production monitoring.
[mlprojects, machinelearningengineer, genai, fine-tuning, ragchatbot, mlportfolio, endtoendpipeline, multimodalai, ai2025, llmengineer, mljobs, mlworkflow, productionai]
@the.datascience.gal
288.0K
PRPreprocessing pipeline for llm
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday
@priyal.py
3.9M
1.1M
THHere’s a roadmap to help you go from a software engineer to a data scientist 👩💻 👇
If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you.
Step 1 – Learn Python and SQL (not Java, C++, or JavaScript).
→ Focus on pandas, numpy, scikit-learn, matplotlib
→ For SQL: use LeetCode or StrataScratch to practice real-world queries
→ Don’t just write code—learn to think in data
Step 2 – Build your foundation in statistics + math.
→ Start with Practical Statistics for Data Scientists
→ Learn: probability, hypothesis testing, confidence intervals, distributions
→ Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule)
Step 3 – Learn ML the right way.
→ Do Andrew Ng’s ML course (Deeplearning.ai)
→ Master the full pipeline: cleaning → feature engineering → modeling → evaluation
→ Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper
Step 4 – Build 2–3 real, messy projects.
→ Don’t follow toy tutorials
→ Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio
→ Upload everything to GitHub with a clear README
Step 5 – Become a storyteller with data.
→ Read Storytelling with Data by Cole Knaflic
→ Learn to explain your findings to non-technical teams
→ Practice communicating precision/recall/F1 in simple language
Step 6 – Stay current. Never stop learning.
→ Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn
→ Join communities, follow researchers, and keep shipping new experiments
-------
Save this for later.
Tag a friend who’s trying to make the switch.
[software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
@the.datascience.gal
445.1K
CSdata structures & algorithms #cscareeradvice #softwareengineerjobs #datastructuresandalgorithms
@cs.aar0n
4.6M
CALevel up your skills for FREE! 🎯 Complete these 6 courses and earn certificates from Google, Microsoft, IBM & W3Schools without paying a single rupee. Start today and boost your career!
#FreeCourses #LearnOnline #SkillUp #CareerGrowth #GoogleAI #MicrosoftAI #PythonDeveloper #DataScience #MachineLearning #CyberSecurity #UpskillForFree #OnlineLearning #Certifications #CareerBoost
@careerwithriya
486.9K
VIfollow + comment “roadmap” for a 5-page guide on learning the math behind AI/ML/datascience.
#cs #learn #ai #datascience #math
#faang #college #quant #techinternships #leetcode
@vinnysaucee
39.9K
HE📌 “Confused about how to start your Machine Learning & AI journey? Here’s your complete roadmap from zero to job-ready! 💻✨”
No more scrolling through 100 videos — this 30 sec guide has everything you need to start & grow in ML!
Save 🔖 | Share 🤝 | Follow @helloworld_avani for more!
#machinelearning #artificialintelligence #pythonforbeginners #datasciencelearning #mlroadmap #techreels #codingjourney #learnwithme #careerinttech #reelsforstudents #studygramindia #trending #explorepage
@helloworld_avani
166.2K
ASTbh after being a Data Scientist for 6 years, I still don’t know some stuff on that 2nd list 😅
Trying to learn ALL of Python at once is so intimidating
Don’t put that pressure on yourself.
Instead only focus on these must-know concepts, and you can ignore stuff on the “Not Now” list for now.
MUST KNOW PYTHON CONCEPTS
• Basic syntax: variables, data types, loops
• Writing custom functions
• Lists, tuples, dictionaries
• List comprehensions
• String manipulation
• Reading and writing files
• Try/except error handling
• Importing and using libraries
• Pandas basics – Series vs DataFrame
• Selecting and filtering data
• Groupby and aggregations
• Merging or joining data
• Sorting and ranking data
• Handling missing values
• Basic plotting – matplotlib
• Working with dates – e.g. pd.to_datetime, .dt
NOT NOW
• Object oriented programming – classes, inheritance
• Generators and decorators
• Custom context managers
• Writing modules or packages
• Virtual environments and dependency management
• Multiprocessing or multithreading
• Async programming
• Advanced pandas tuning – eval, query
• Unit testing and CI/CD
• Custom exception classes
• Functional programming tricks – map, reduce, lambdas everywhere
• Building web APIs – Flask, FastAPI
#python #datascience #datascientist #datascienceinterview
@askdatadawn
✨ #Datascience Learning Keşif Rehberi
Instagram'da #Datascience Learning etiketi altında 86K 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.
En yeni #Datascience Learning videolarını keşfetmeye hazır mısınız? Bu etiket altında paylaşılan en etkileyici içerikleri, giriş yapmanıza gerek kalmadan görüntüleyin. Şu an @careerwithriya, @heenakouser.me and @setupsai tarafından paylaşılan Reels videoları toplulukta büyük ilgi görüyor.
#Datascience Learning 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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📹 Video Trendleri: En yeni Reels içeriklerini ve viral videoları keşfedin
📈 Hashtag Stratejisi: İçerikleriniz için trend hashtag seçeneklerini inceleyin
🌟 Öne Çıkanlar: @careerwithriya, @heenakouser.me, @setupsai ve diğerleri topluluğa yön veriyor
#Datascience Learning Hakkında SSS
Pictame ile Instagram'a giriş yapmadan tüm #Datascience Learning reels ve videolarını izleyebilirsiniz. İzleme aktiviteniz tamamen gizli kalır - hiçbir iz bırakılmaz, hesap gerekmez. Hashtag'i aratın ve trend içerikleri anında keşfetmeye başlayın.
İçerik Performans Analizi
12 reel analizi
💡 En iyi performans gösteren içerikler ortalama 2.9M görüntüleme alıyor (ortalamadan 2.5x fazla). Orta seviye rekabet - düzenli paylaşım momentum oluşturur.
Kitlenizin en aktif olduğu saatlerde haftada 3-5 kez düzenli paylaşım yapın
İçerik Oluşturma İpuçları & Strateji
🔥 #Datascience Learning yüksek etkileşim potansiyeli gösteriyor - peak saatlerde stratejik paylaşım yapın
📹 #Datascience Learning için yüksek kaliteli dikey videolar (9:16) en iyi performansı gösteriyor - iyi aydınlatma ve net ses kullanın
✍️ Hikayeli detaylı açıklamalar işe yarıyor - ortalama açıklama uzunluğu 490 karakter
✨ Çok sayıda onaylı hesap aktif (%58) - ilham almak için içerik tarzlarını inceleyin
#Datascience Learning İle İlgili Popüler Aramalar
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Datascience Learning Keşfet#learning#datascience#learn#learnings#learned#learns#learnly#learnedly