#Databricks Visualizations

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#Databricks Visualizations Reel by @viktoria.semaan (verified account) - Free. Forever. For Everyone. Link in captions โ†“

Databricks removed all the gatekeeping from Data & AI!

โŒ No "business email" required
โŒ No credit ca
180.4K
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@viktoria.semaan
Free. Forever. For Everyone. Link in captions โ†“ Databricks removed all the gatekeeping from Data & AI! โŒ No โ€œbusiness emailโ€ required โŒ No credit card โŒ No expiring credits Now anyone can get hands-on with the same cutting-edge platform used by the worldโ€™s top data teams โ€” free, forever. Perfect for learning, prototyping, and building real skills. What you get: โœ… Unified Data & AI workspace โ€” SQL, Python, dashboards & ML in one place ๐Ÿ““ Collaborative notebooks โ€” built-in versioning & comments ๐Ÿ“Š Sample datasets + Genie โ€” query with SQL or natural language ๐Ÿงฑ Lakehouse + LakeFlow โ€” ingest, transform, orchestrate visually ๐Ÿค– AI apps & agents โ€” experiment with open models instantly ๐Ÿง  Databricks Assistant โ€” live code suggestions & explanations ๐ŸŽ“ Self-paced training โ€” Data Engineering, ML & GenAI ๐Ÿ”— Sign up now: bit.ly/dbx-free-signup Comment DATA and Iโ€™ll send you links to self-paced courses! This is your chance to build skills in the technology thatโ€™s defining the future. ๐Ÿ“Œ Save & Share with others! โ€”โ€” Hello ๐Ÿ‘‹ Iโ€™m Viktoria, AI Engineer and Principal Technologist at Databricks. I share practical AI tips and resources. Follow for more educational content @viktoria.semaan #ai #databricks #freetutorials #genai
#Databricks Visualizations Reel by @iammoureen_ - Manual Data Tree is just a demo to understand the splitting and get the real image of the process. It is NOT suitable for Big Data โŒ๏ธ๐Ÿ™…โ€โ™€๏ธ

AI Wizards
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@iammoureen_
Manual Data Tree is just a demo to understand the splitting and get the real image of the process. It is NOT suitable for Big Data โŒ๏ธ๐Ÿ™…โ€โ™€๏ธ AI Wizards session 4 โœ…๏ธ @aiwizards_eastc
#Databricks Visualizations Reel by @codeaiwithsaugat - Beginners don't fail AI.
They overthink datasets.
Kaggle = download โ†’ analyze โ†’ build ๐Ÿš€
Stop hunting data and start learning.
Day 5 of 30 Days 30 AI
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@codeaiwithsaugat
Beginners donโ€™t fail AI. They overthink datasets. Kaggle = download โ†’ analyze โ†’ build ๐Ÿš€ Stop hunting data and start learning. Day 5 of 30 Days 30 AI Concepts Follow for daily AI clarity ๐Ÿ‘‡ #AI #MachineLearning #Kaggle #DataScience #LearnAI
#Databricks Visualizations Reel by @datasciencebrain (verified account) - I wish someone had sent me this list when I started. ๐Ÿค–

Save this post before you forget. ๐Ÿ”– 

75 YouTube channels that cover everything - from Pytho
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@datasciencebrain
I wish someone had sent me this list when I started. ๐Ÿค– Save this post before you forget. ๐Ÿ”– 75 YouTube channels that cover everything โ€” from Python to Kubernetes, DSA to Cloud, ML to UI Design. All free. All world-class. ๐Ÿง  Here's the breakdown ๐Ÿ‘‡ Stop paying for courses when the internet is literally handing you this for free. ๐Ÿ’ก Tag a friend who's learning to code. ๐Ÿ‘‡ ๐Ÿ“ฒ Follow @datasciencebrain for Daily Notes ๐Ÿ“, Tips โš™๏ธ and Interview QA๐Ÿ† . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering
#Databricks Visualizations Reel by @deejabuildstech - I didn't want to just use Generative AI.
I wanted to understand how it actually works.

So I started from the fundamentals:
LLMs, encoders vs decoders
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@deejabuildstech
I didnโ€™t want to just use Generative AI. I wanted to understand how it actually works. So I started from the fundamentals: LLMs, encoders vs decoders, tokenization, APIs, and how models process input before generating output. What surprised me most? AI isnโ€™t magic. Itโ€™s structured systems + math + patterns. As a Data Engineer, learning this doesnโ€™t feel like starting over. It feels like leveling up. Iโ€™m documenting my journey from Data โ†’ AI as I learn in real time. If youโ€™re in data and thinking about learning AI too โ€” youโ€™re not late. Youโ€™re early. Follow along. #GenerativeAI #DataEngineer #AIJourney #WomenInTech #BuildInPublic MachineLearning TechLearning
#Databricks Visualizations Reel by @dorisgold_aigirly - @quinmhimee you asked for it and here you go๐Ÿฅฐ 
Also these tools aren't exhaustive, there's way more out there. Do your research and find what fits YO
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@dorisgold_aigirly
@quinmhimee you asked for it and here you go๐Ÿฅฐ Also these tools arenโ€™t exhaustive, thereโ€™s way more out there. Do your research and find what fits YOUR workflow best. Smart professionals are moving beyond generic AI. Theyโ€™re using specialized tools built for their exact role. What AI tool are you currently using for your role? Follow for more role-specific AI tools. This series isnโ€™t over yet Oh and @that_tech_baddie I know youโ€™re a data analyst and you may just find these tools helpfulโ€ฆ you can also share it with your tech girlieโ€™s community too๐Ÿฅฐ๐Ÿฅฐ๐Ÿฅฐ #AITools #aifordataanalytics #dataanalyst
#Databricks Visualizations Reel by @datasciencebrain (verified account) - The only Data Science & AI cheat sheet you'll ever need ๐Ÿ”ฅ

โฌ‡๏ธ Want the full PDF cheat sheet for FREE?
Comment "CHEAT" below ๐Ÿ‘‡

300+ functions. 8 lib
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@datasciencebrain
The only Data Science & AI cheat sheet you'll ever need ๐Ÿ”ฅ โฌ‡๏ธ Want the full PDF cheat sheet for FREE? Comment "CHEAT" below ๐Ÿ‘‡ 300+ functions. 8 libraries. Real code examples. ๐Ÿผ Pandas โ€” 70+ functions with examples ๐Ÿ”ข NumPy โ€” Array ops, linear algebra & more ๐Ÿ—„๏ธ SQL โ€” Joins, window functions, CTEs ๐Ÿ“Š Excel โ€” XLOOKUP, dynamic arrays, LAMBDA ๐Ÿ“ˆ Matplotlib โ€” Every chart type covered ๐Ÿค– Scikit-Learn โ€” Full ML pipeline in one sheet ๐Ÿ”ฅ PyTorch โ€” Tensors to training loops ๐Ÿฆœ LangGraph โ€” Agents, memory, HITL & tools This is the resource I wish I had when I started ๐Ÿ“ŒSave this post, you WILL need it later ๐Ÿ“ฒ Follow @datasciencebrain for Daily Notes ๐Ÿ“, Tips โš™๏ธ and Interview QA๐Ÿ† . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering
#Databricks Visualizations Reel by @datasciencebrain (verified account) - Master these 10 libraries and companies will pay you $150K+ to build their AI products.

Saved this yet? ๐Ÿ“Œ ๏ฟฝ

Want the complete 92 page cheat sheet w
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@datasciencebrain
Master these 10 libraries and companies will pay you $150K+ to build their AI products. Saved this yet? ๐Ÿ“Œ ๏ฟฝ Want the complete 92 page cheat sheet with code examples? ๐Ÿ‘‰ Follow me(so i can message) & Comment , I'll send it to you in DM๐Ÿ“ฅ 1. LangGraph Build stateful AI agents with complex workflows 2. Instructor Structured outputs from LLMs using Pydantic 3. LlamaIndex RAG systems for document Q&A 4. OpenAI SDK Access GPT models with function calling 5. FastAPI Deploy AI models as fast APIs 6. Anthropic SDK Claude integration for complex reasoning 7. DSPy Auto-optimize prompts like ML models 8. ChromaDB Simple vector database for embeddings 9. LiteLLM Unified interface for 100+ LLMs 10. Transformers Access & fine-tune open-source models ๐Ÿ“ฒ Follow @datasciencebrain for Daily Notes ๐Ÿ“, Tips โš™๏ธ and Interview QA๐Ÿ† . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering
#Databricks Visualizations Reel by @thenadcoder (verified account) - Lets build a simple AI project.  When you see words like rags, fine tuning, it sounds scary.  So, in today's video, lets build a simple AI project.  N
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@thenadcoder
Lets build a simple AI project. When you see words like rags, fine tuning, it sounds scary. So, in todayโ€™s video, lets build a simple AI project. No code but something that will help you get over the fear and build your own projects. This is a step by step guide so follow along or save this video. What should I build next? Let me know in comments. Also did you like this video?
#Databricks Visualizations Reel by @dataanddreamscapes - Day 5: Built a Binary Purchase Label, Joined with Silver Features, Split train/test, and Validated Class Distribution. Moving from Feature Engineering
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@dataanddreamscapes
Day 5: Built a Binary Purchase Label, Joined with Silver Features, Split train/test, and Validated Class Distribution. Moving from Feature Engineering to AI System Preparation. #databrickswithidc #DataEngineering #databricks #featureengineering #training Activity Log: https://pewter-porch-d86.notion.site/Databricks-14-Days-AI-Challenge-2-30947a4b88b880a5a363f2181e43601e?source=copy_link
#Databricks Visualizations Reel by @aiwithdivyam (verified account) - Comment to get the link.
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[ data science projects, machine learning, nlp, ai, generative ai, github repo, free resource ]
214.0K
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@aiwithdivyam
Comment to get the link. . . [ data science projects, machine learning, nlp, ai, generative ai, github repo, free resource ]
#Databricks Visualizations Reel by @mrk_talkstech (verified account) - How I do Data Engineering tasks using AI?

#AIForDataEngineering #DataEngineering #AIAutomation #GenAI #ModernDataStack

AI for data engineering, AI d
3.0K
MR
@mrk_talkstech
How I do Data Engineering tasks using AI? #AIForDataEngineering #DataEngineering #AIAutomation #GenAI #ModernDataStack AI for data engineering, AI data pipelines, automated ETL, schema generation with AI, SQL query generation, PySpark code generation, data validation automation, pipeline debugging with AI, log analysis using AI, data modeling assistance, documentation generation, code refactoring, test case generation, data quality checks, metadata extraction, AI copilots, prompt engineering for data, AI workflow automation, cloud data automation, productivity boost with AI

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๐Ÿ”ฅ Highly Competitive

๐Ÿ’ก Top performing posts average 199.1K views (2.8x above average). High competition - quality and timing are critical.

Focus on peak engagement hours (typically 11 AM-1 PM, 7-9 PM) and trending formats

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โœ๏ธ Detailed captions with story work well - average caption length is 606 characters

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