A–ZDictionaryofData.pdf
1008.6 KB
Data is everywhere. Clarity is rare.
Behind every dashboard, SQL query, or machine learning model lies a common challenge — understanding the language of data.
The 𝐀–𝐙 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 brings together 500+ essential terms across SQL, Python, Power BI, Excel, Statistics, and Machine Learning in one structured reference.
This is the layer many professionals underestimate.
Not tools. Not dashboards.
But the ability to understand, interpret, and communicate concepts with precision.
𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞:
- Clear definitions without unnecessary complexity
- Concepts connected across tools and domains
- Coverage from foundational terms to advanced analytics concepts
- Useful for both technical execution and business communication
𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥:
- During interviews, when explaining concepts matters more than just knowing them
- In projects, where misinterpreting a term can lead to incorrect insights
- In stakeholder discussions, where clarity builds credibility
- In learning journeys, where structured understanding accelerates growth
𝐒𝐭𝐫𝐨𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐢𝐭𝐬 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞.
#DataAnalytics #BusinessIntelligence #DataScience #SQL #Python #PowerBI #Excel #MachineLearning #Statistics #DataEngineering #AnalyticsCareer #DataLearning #DataProfessionals #CareerGrowth #InterviewPreparation
https://xn--r1a.website/DataAnalyticsX
Behind every dashboard, SQL query, or machine learning model lies a common challenge — understanding the language of data.
The 𝐀–𝐙 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 brings together 500+ essential terms across SQL, Python, Power BI, Excel, Statistics, and Machine Learning in one structured reference.
This is the layer many professionals underestimate.
Not tools. Not dashboards.
But the ability to understand, interpret, and communicate concepts with precision.
𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞:
- Clear definitions without unnecessary complexity
- Concepts connected across tools and domains
- Coverage from foundational terms to advanced analytics concepts
- Useful for both technical execution and business communication
𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥:
- During interviews, when explaining concepts matters more than just knowing them
- In projects, where misinterpreting a term can lead to incorrect insights
- In stakeholder discussions, where clarity builds credibility
- In learning journeys, where structured understanding accelerates growth
𝐒𝐭𝐫𝐨𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐢𝐭𝐬 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞.
#DataAnalytics #BusinessIntelligence #DataScience #SQL #Python #PowerBI #Excel #MachineLearning #Statistics #DataEngineering #AnalyticsCareer #DataLearning #DataProfessionals #CareerGrowth #InterviewPreparation
https://xn--r1a.website/DataAnalyticsX
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LLMs are the new operating system for work. 🚀💻
But most people still don’t know the difference between RAG, Embeddings, and Hallucinations. 🤔🧠
Here’s the vocabulary cheat sheet everyone in AI should know 📚✨
These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know 👩💼👨💻🎨🚀
#LLM #DataScience #AI #ML
https://xn--r1a.website/DataAnalyticsX📎
But most people still don’t know the difference between RAG, Embeddings, and Hallucinations. 🤔🧠
Here’s the vocabulary cheat sheet everyone in AI should know 📚✨
These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know 👩💼👨💻🎨🚀
#LLM #DataScience #AI #ML
https://xn--r1a.website/DataAnalyticsX
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AI for Data Processing and Analytics 🤖📊
Hex — a platform that helps analyze data through SQL and Python, automating most routine tasks 🚀💻
What it can do: ✨🛠
• generate SQL queries and Python code 💾🧩
• build charts and dashboards 📈📉
• explain results and answer questions in simple language 🗣🧠
• allow you to quickly create a report or a data app 📝📱
Link: https://hex.tech/ 🔗🌐
#DataAnalytics #HexTech #SQL #Python #Automation #DataScience
https://xn--r1a.website/DataAnalyticsX✈️
Hex — a platform that helps analyze data through SQL and Python, automating most routine tasks 🚀💻
What it can do: ✨🛠
• generate SQL queries and Python code 💾🧩
• build charts and dashboards 📈📉
• explain results and answer questions in simple language 🗣🧠
• allow you to quickly create a report or a data app 📝📱
Link: https://hex.tech/ 🔗🌐
#DataAnalytics #HexTech #SQL #Python #Automation #DataScience
https://xn--r1a.website/DataAnalyticsX
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Cheat sheet for working with data in Python (Data Science) 🐍📊
🔹 importing NumPy and pandas libraries — basic tools for data processing 🛠️
🔹 text files — reading/writing plain text and working via context manager 📄
🔹 tabular CSV/flat files — loading and processing structured data into DataFrame 📊
🔹 Excel files — working with sheets and tables 📑
🔹 SAS/Stata files — importing statistical formats 📉
🔹 HDF5 and Pickle — saving and loading complex data structures 💾
🔹 MATLAB files — reading .mat via SciPy 🧮
🔹 relational databases (SQL) — connecting, querying, and converting results into DataFrame 🗄️
🔹 Python dictionaries — accessing keys, values, and nested structures 🔑
🔹 data exploration (NumPy arrays and pandas DataFrames) — viewing types, sizes, and basic statistics 🔍
🔹 file system navigation — magic commands and os module for working with files and directories 📂
#Python #DataScience #Coding #Programming #Tech #Learning
https://xn--r1a.website/DataAnalyticsX✅
🔹 importing NumPy and pandas libraries — basic tools for data processing 🛠️
🔹 text files — reading/writing plain text and working via context manager 📄
🔹 tabular CSV/flat files — loading and processing structured data into DataFrame 📊
🔹 Excel files — working with sheets and tables 📑
🔹 SAS/Stata files — importing statistical formats 📉
🔹 HDF5 and Pickle — saving and loading complex data structures 💾
🔹 MATLAB files — reading .mat via SciPy 🧮
🔹 relational databases (SQL) — connecting, querying, and converting results into DataFrame 🗄️
🔹 Python dictionaries — accessing keys, values, and nested structures 🔑
🔹 data exploration (NumPy arrays and pandas DataFrames) — viewing types, sizes, and basic statistics 🔍
🔹 file system navigation — magic commands and os module for working with files and directories 📂
#Python #DataScience #Coding #Programming #Tech #Learning
https://xn--r1a.website/DataAnalyticsX
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⚡️ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" 🤖
A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. 🚀
Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. 🧠
The roadmap is divided into 7 tracks: 📊
1. Foundation: Python, mathematics, statistics, tools 🏗️
2. Classic ML: scikit-learn, tabular data, metrics, validation 📈
3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖
5. Generative AI: images, videos, audio, multimodality 🎨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️
7. Specialization: CV, NLP, RecSys, RL, Safety 🎯
The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫
In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. 🛠️
A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. 🛑
In terms of time, it's no fairy tale either: ⏳
1. 0-3 months: mathematics, classic ML 📚
2. 3-6 months: Deep Learning and PyTorch 🔥
3. 6-12 months: LLM, RAG, fine-tuning, AI agents 🤖
4. 12+ months: MLOps, production, scaling, specialization 🚀
Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! 🎓
If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. 🗺️
https://github.com/justxor/MachineLearningRoadmap 🔗
#MachineLearning #AI #DataScience #LLM #MLOps #Python
A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. 🚀
Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. 🧠
The roadmap is divided into 7 tracks: 📊
1. Foundation: Python, mathematics, statistics, tools 🏗️
2. Classic ML: scikit-learn, tabular data, metrics, validation 📈
3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖
5. Generative AI: images, videos, audio, multimodality 🎨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️
7. Specialization: CV, NLP, RecSys, RL, Safety 🎯
The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫
In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. 🛠️
A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. 🛑
In terms of time, it's no fairy tale either: ⏳
1. 0-3 months: mathematics, classic ML 📚
2. 3-6 months: Deep Learning and PyTorch 🔥
3. 6-12 months: LLM, RAG, fine-tuning, AI agents 🤖
4. 12+ months: MLOps, production, scaling, specialization 🚀
Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! 🎓
If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. 🗺️
https://github.com/justxor/MachineLearningRoadmap 🔗
#MachineLearning #AI #DataScience #LLM #MLOps #Python
GitHub
GitHub - justxor/MachineLearningRoadmap: Полный Roadmap по машинному обучению 2026
Полный Roadmap по машинному обучению 2026 . Contribute to justxor/MachineLearningRoadmap development by creating an account on GitHub.
❤3
Forwarded from Machine Learning
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
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Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.
Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.
Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
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Forwarded from Machine Learning with Python
Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
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The ultimate guide to fine tuning.pdf
15.2 MB
🔖 The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. 📚
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
A free 115-page book dedicated to the retraining of large language models. 📚
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤2
Forwarded from Machine Learning with Python
A new collection of free courses has been added:
🔗 https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠
What's inside:
• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes
The idea was appreciated. 👍
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
🔗 https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠
What's inside:
• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes
The idea was appreciated. 👍
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
GitHub
GitHub - dair-ai/ML-Course-Notes: 🎓 Sharing machine learning course / lecture notes.
🎓 Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes
❤1
Forwarded from Machine Learning with Python
10 GitHub repositories that are worth checking out for an AI engineer 🤖
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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Forwarded from Machine Learning with Python
Learn AI for free directly from top companies. 🚀
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn
💬 Comment "Learning" if you find this helpful.
🔄 Repost so others can take help.
🔖 Must bookmark for future reference.
#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://xn--r1a.website/CodeProgrammer
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn
💬 Comment "Learning" if you find this helpful.
🔄 Repost so others can take help.
🔖 Must bookmark for future reference.
#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://xn--r1a.website/CodeProgrammer
Grow with Google US
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🔖 Building our own GPT-like model in PyTorch
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Do you want to quickly improve your SQL skills? 🚀
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https://programmingvalley.com/course/ibm-data-science-free-course
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They are suitable for both learning the basics of SQL and mastering data analysis, BI tools, and working with data in real projects. 💻📊
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Here are 4 courses that will help you level up:
- SQL Basics for Data Science
https://programmingvalley.com/course/learn-sql-basics-for-data-science-free-course
- Google Data Analytics
https://programmingvalley.com/course/google-data-analytics-free-course
- IBM Data Science
https://programmingvalley.com/course/ibm-data-science-free-course
- Google Business Intelligence
https://programmingvalley.com/course/google-business-intelligence-free-course
They are suitable for both learning the basics of SQL and mastering data analysis, BI tools, and working with data in real projects. 💻📊
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Programming Valley
Learn SQL Basics for Data Science | Programming Valley
Course Overview This hands-on SQL specialization helps you build the foundational skills needed to analyze, transform, and manage data using SQL — the most in-demand language for data professionals.You’ll start from zero and progress through practical exercises…
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Curates LLM tools and research for scientific discovery 🧬🔬
Repo: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery 🔗🚀
#LLM #ScientificDiscovery #ResearchTools #AI #MachineLearning #DataScience
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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Repo: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery 🔗🚀
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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