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MAAmazing websites (part 96) visit uncensored.com the #1 uncensored AI chatbot #websites #aitools
@maskedguyai
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SEPowerful websites you should know (part 827) thousands of courses in almost any field #study #learn #programming
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_RFree webinar😱⁉️
AI Machine learning & Data science
#rtr #rtrvlogs
@_rtr_vlogs_
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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
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PRPreprocessing pipeline for llm
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday
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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
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CSdata structures & algorithms #cscareeradvice #softwareengineerjobs #datastructuresandalgorithms
@cs.aar0n
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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
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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
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