๐๐ณ๐ฎ๐ซ๐_๐๐๐ญ๐_๐๐ง๐ ๐ข๐ง๐๐๐ซ.pdf
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Everyone wants to become a ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซโฆ ๐ But very few follow a structured path. ๐ค
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โ๏ธ Mastering SQL & Python (not just basics) ๐ป
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โ๏ธ Building real-world, portfolio-ready projects ๐
โ๏ธ Preparing specifically for interviews๐ฏ
โ๏ธ Learning with a focused community๐ค
They keep learning random tools, watching endless tutorials and still feel unprepared. ๐คฏ
Meanwhile, some people are quietly transitioning into roles like:
๐ผ Azure Data Engineer
๐ผ Data Architect
๐ผ Senior Data Engineer
What are they doing differently? ๐ค
Theyโre not doing more.
Theyโre doing the right things consistently. โจ
Hereโs whatโs working for them:
โ๏ธ A step-by-step Azure Data Engineering roadmap ๐บ
โ๏ธ Mastering SQL & Python (not just basics) ๐ป
โ๏ธ Hands-on with Azure tools (ADF, Synapse, Data Lake) โ๏ธ
โ๏ธ Building real-world, portfolio-ready projects ๐
โ๏ธ Preparing specifically for interviews
โ๏ธ Learning with a focused community
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Forwarded from Machine Learning with Python
๐ Thrilled to announce a major milestone in our collective upskilling journey! ๐
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I am incredibly excited to share a curated ecosystem of high-impact resources focused on Machine Learning and Artificial Intelligence. By consolidating a comprehensive library of PDFsโfrom foundational onboarding to advanced strategic insightsโinto a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. ๐โจ
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. ๐ก๐
โ๏ธ Unlock your potential here:
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LLM Engineering Roadmap (2026 Practical Guide) ๐บโจ
If your goal is to build real LLM apps (not just prompts), follow this order. ๐
1๏ธโฃ Python + APIs ๐๐
Youโll spend most of your time wiring systems.
Learn:
โ functions, classes
โ working with APIs (requests, JSON)
โ async basics
โ environment variables
Resources
โ Python for Everybody
https://lnkd.in/gUqkvnGG
โ Introduction to Python
https://lnkd.in/g7xfYJVZ
โ MLTUT Python Basics Course
https://lnkd.in/gCqfyCGZ
2๏ธโฃ Text Basics (NLP) ๐๐ง
You donโt need heavy theory, just the essentials.
Learn:
โ tokenization
โ text cleaning
โ similarity (cosine)
โ basic embeddings idea
Resources
โ Natural Language Processing Specialization
https://lnkd.in/gz_xmqD9
โ NLP in Python
https://lnkd.in/gnpcJxhz
3๏ธโฃ Transformers (Whatโs happening behind the API) ๐ค๐
Enough to not treat it like a black box.
Learn:
โ tokens, context window
โ attention (high level)
โ why embeddings work
โ limits of LLMs
Resources
โ Generative AI with Large Language Models
https://lnkd.in/gk3PPtyf
โ Hugging Face Transformers Course
https://lnkd.in/ggSR5JNb
4๏ธโฃ Prompting (Make outputs reliable) ๐ฌ๐ฏ
Treat prompts like code.
Learn:
โ few-shot examples
โ structured outputs (JSON)
โ system vs user instructions
โ simple evals (does it break?)
Resources
โ Prompt Engineering for ChatGPT
https://lnkd.in/gyg4EiJS
โ Prompt Engineering with LLMs
https://lnkd.in/gn67Mxga
5๏ธโฃ Embeddings + Vector DBs ๐๐
This is how you add your data.
Learn:
โ embedding generation
โ similarity search
โ indexing
Tools:
โ FAISS
โ Pinecone
โ Chroma
Resources
โ Working with Embeddings
https://lnkd.in/gnngPW4E
โ Vector Databases & Semantic Search
https://lnkd.in/gP2HdMmD
6๏ธโฃ RAG Pipelines ๐๐
Most useful apps use this pattern.
Learn:
โ chunking documents
โ retrieval + ranking
โ prompt + context design
โ basic evaluation
Resources
โ Generative AI for Software Development
https://lnkd.in/g3uduecv
โ Build RAG Apps with LangChain
https://lnkd.in/ggXJjgDN
7๏ธโฃ Build Real Applications ๐ ๐ป
Keep them small and usable.
Build:
โ document Q&A (PDF โ answers)
โ internal knowledge bot
โ code assistant (repo Q&A)
โ support chatbot
Tools:
โ LangChain
โ LlamaIndex
โ OpenAI APIs
Resources
โ Build LLM Apps with LangChain & Python
https://lnkd.in/g6xXVX_8
โ LLM Applications
https://lnkd.in/gzs8_SRk
8๏ธโฃ Deployment ๐ขโ๏ธ
Make it usable by others.
Learn:
โ FastAPI endpoints
โ streaming responses
โ caching (reduce cost)
โ logging + monitoring
Tools:
โ FastAPI
โ Docker
โ AWS / GCP
Resources
โMachine Learning Engineering for Production (MLOps)
https://lnkd.in/gCMtYSk5
โ MLOps Fundamentals
https://lnkd.in/g8TGrUzT
https://t.iss.one/DataAnalyticsXโ
If your goal is to build real LLM apps (not just prompts), follow this order. ๐
1๏ธโฃ Python + APIs ๐๐
Youโll spend most of your time wiring systems.
Learn:
โ functions, classes
โ working with APIs (requests, JSON)
โ async basics
โ environment variables
Resources
โ Python for Everybody
https://lnkd.in/gUqkvnGG
โ Introduction to Python
https://lnkd.in/g7xfYJVZ
โ MLTUT Python Basics Course
https://lnkd.in/gCqfyCGZ
2๏ธโฃ Text Basics (NLP) ๐๐ง
You donโt need heavy theory, just the essentials.
Learn:
โ tokenization
โ text cleaning
โ similarity (cosine)
โ basic embeddings idea
Resources
โ Natural Language Processing Specialization
https://lnkd.in/gz_xmqD9
โ NLP in Python
https://lnkd.in/gnpcJxhz
3๏ธโฃ Transformers (Whatโs happening behind the API) ๐ค๐
Enough to not treat it like a black box.
Learn:
โ tokens, context window
โ attention (high level)
โ why embeddings work
โ limits of LLMs
Resources
โ Generative AI with Large Language Models
https://lnkd.in/gk3PPtyf
โ Hugging Face Transformers Course
https://lnkd.in/ggSR5JNb
4๏ธโฃ Prompting (Make outputs reliable) ๐ฌ๐ฏ
Treat prompts like code.
Learn:
โ few-shot examples
โ structured outputs (JSON)
โ system vs user instructions
โ simple evals (does it break?)
Resources
โ Prompt Engineering for ChatGPT
https://lnkd.in/gyg4EiJS
โ Prompt Engineering with LLMs
https://lnkd.in/gn67Mxga
5๏ธโฃ Embeddings + Vector DBs ๐๐
This is how you add your data.
Learn:
โ embedding generation
โ similarity search
โ indexing
Tools:
โ FAISS
โ Pinecone
โ Chroma
Resources
โ Working with Embeddings
https://lnkd.in/gnngPW4E
โ Vector Databases & Semantic Search
https://lnkd.in/gP2HdMmD
6๏ธโฃ RAG Pipelines ๐๐
Most useful apps use this pattern.
Learn:
โ chunking documents
โ retrieval + ranking
โ prompt + context design
โ basic evaluation
Resources
โ Generative AI for Software Development
https://lnkd.in/g3uduecv
โ Build RAG Apps with LangChain
https://lnkd.in/ggXJjgDN
7๏ธโฃ Build Real Applications ๐ ๐ป
Keep them small and usable.
Build:
โ document Q&A (PDF โ answers)
โ internal knowledge bot
โ code assistant (repo Q&A)
โ support chatbot
Tools:
โ LangChain
โ LlamaIndex
โ OpenAI APIs
Resources
โ Build LLM Apps with LangChain & Python
https://lnkd.in/g6xXVX_8
โ LLM Applications
https://lnkd.in/gzs8_SRk
8๏ธโฃ Deployment ๐ขโ๏ธ
Make it usable by others.
Learn:
โ FastAPI endpoints
โ streaming responses
โ caching (reduce cost)
โ logging + monitoring
Tools:
โ FastAPI
โ Docker
โ AWS / GCP
Resources
โMachine Learning Engineering for Production (MLOps)
https://lnkd.in/gCMtYSk5
โ MLOps Fundamentals
https://lnkd.in/g8TGrUzT
https://t.iss.one/DataAnalyticsX
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This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
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So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint โ it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from โjust an imageโ into a digital agent.
Public mint: today, 16:00
Price: 50 TON
๐ Mint your Lobster on Getgems ๐ฆ๐ฆ๐ฆ
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint โ it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from โjust an imageโ into a digital agent.
Public mint: today, 16:00
Price: 50 TON
๐ Mint your Lobster on Getgems ๐ฆ๐ฆ๐ฆ
โค2
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Top-10 Stanford Courses on AI and Machine Learning - directly from official pages and materials.
CS221: Artificial Intelligence
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CS229: Machine Learning
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CS229M: Theory of Machine Learning
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CS230: Deep Learning
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CS234: Reinforcement Learning
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CS224N: Natural Language Processing
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CS231N: Deep Learning for Computer Vision
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CME295: Large Language Models
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CS236: Deep Generative Models
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CS336: Modeling Language from Scratch
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These courses cover the basics: from classic machine learning to modern LLMs and generative models, including both practice and theory.
CS221: Artificial Intelligence
https://www.youtube.com/playlist?list=PLoROMvodv4rOca_Ovz1DvdtWuz8BfSWL2
CS229: Machine Learning
https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
CS229M: Theory of Machine Learning
https://www.youtube.com/playlist?list=PLoROMvodv4rP8nAmISxFINlGKSK4rbLKh
CS230: Deep Learning
https://www.youtube.com/playlist?list=PLoROMvodv4rNRRGdS0rBbXOUGA0wjdh1X
CS234: Reinforcement Learning
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CS224N: Natural Language Processing
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CS231N: Deep Learning for Computer Vision
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CME295: Large Language Models
https://www.youtube.com/playlist?list=PLoROMvodv4rObv1FMizXqumgVVdzX4_05
CS236: Deep Generative Models
https://www.youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8
CS336: Modeling Language from Scratch
https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_
These courses cover the basics: from classic machine learning to modern LLMs and generative models, including both practice and theory.
โค3
Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
โค1
Forwarded from Machine Learning with Python
GitHub repositories to enhance your Python proficiency:
- Web development with Django โ https://github.com/django/django
- Data Science tools โ https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges โ https://github.com/TheAlgorithms/Python
- Machine learning recipes โ https://github.com/ageron/handson-ml2
- Testing best practices โ https://github.com/pytest-dev/pytest
- Automation scripts โ https://github.com/soimort/you-get
- Advanced Python concepts โ https://github.com/faif/python-patterns
Bookmark and share
https://t.iss.one/CodeProgrammer๐
- Web development with Django โ https://github.com/django/django
- Data Science tools โ https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges โ https://github.com/TheAlgorithms/Python
- Machine learning recipes โ https://github.com/ageron/handson-ml2
- Testing best practices โ https://github.com/pytest-dev/pytest
- Automation scripts โ https://github.com/soimort/you-get
- Advanced Python concepts โ https://github.com/faif/python-patterns
Bookmark and share
https://t.iss.one/CodeProgrammer
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๐งฎ $40/day ร 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
๐ Join for Free, Click here
#ad๐ข InsideAd
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
๐ Join for Free, Click here
#ad
Please open Telegram to view this post
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