Forwarded from Machine Learning with Python
Machine Learning in python.pdf
1 MB
Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.iss.one/CodeProgrammer || Share🌐 and Like 👍
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.iss.one/CodeProgrammer || Share
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Forwarded from Machine Learning with Python
🚀 Thrilled to announce a major milestone in our collective upskilling journey! 🌟
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 PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
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 PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
❤3
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://t.iss.one/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://t.iss.one/DataAnalyticsX
❤9
⚡️ 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
❤4
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
❤4
Assembling GPT-like LLMs from scratch on PyTorch 🔥
https://github.com/analyticalrohit/llms-from-scratch
📚 10 notebooks. Step-by-step explanation.
🧩 Breaks down the architecture of LLMs into simple parts.
✅ Suitable for beginners.
🛠 Completely hands-on.
#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
https://github.com/analyticalrohit/llms-from-scratch
📚 10 notebooks. Step-by-step explanation.
🧩 Breaks down the architecture of LLMs into simple parts.
✅ Suitable for beginners.
🛠 Completely hands-on.
#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤4
Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇
AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖
It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨
Key features: 🌟
• TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥
• Books section – lists AI/ML/DL books with short notes on where each one helps 📚
• Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓
• Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️
• Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄
Free public GitHub repo. 🆓
https://github.com/ArturoNereu/AI-Study-Group
#AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖
It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨
Key features: 🌟
• TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥
• Books section – lists AI/ML/DL books with short notes on where each one helps 📚
• Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓
• Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️
• Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄
Free public GitHub repo. 🆓
https://github.com/ArturoNereu/AI-Study-Group
#AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤3
🚀 Create an LLM from Scratch!
I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍
✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀
🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu
#LLM #AI #MachineLearning #Python #GenAI #DeepLearning
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍
✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀
🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu
#LLM #AI #MachineLearning #Python #GenAI #DeepLearning
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤5
🔖 Found a huge database on System Design for GenAI and LLM! 🤖📚
500+ real reviews of GenAI, LLM, and ML systems from OpenAI, Anthropic, Google, Microsoft, Netflix, and dozens of other companies. 🌐🏢
A real find for those who are building AI products or want to understand how market leaders do it. 🚀💡
⛓️ Link to GitHub
https://github.com/themanojdesai/genai-llm-ml-case-studies
#SystemDesign #GenAI #LLM #MachineLearning #AI #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
500+ real reviews of GenAI, LLM, and ML systems from OpenAI, Anthropic, Google, Microsoft, Netflix, and dozens of other companies. 🌐🏢
A real find for those who are building AI products or want to understand how market leaders do it. 🚀💡
⛓️ Link to GitHub
https://github.com/themanojdesai/genai-llm-ml-case-studies
#SystemDesign #GenAI #LLM #MachineLearning #AI #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤5