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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
PhD Students - Do you need datasets for your research?
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets – object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset – visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset – dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation – clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset – action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
/channel/CodeProgrammer 👾
How a CNN sees images simplified 🧠
1. Input → Image breaks into pixels (RGB numbers)
2. Feature Extraction
· Convolution → Detects edges/patterns
· ReLU → Kills negatives, adds non-linearity
· Pooling → Shrinks data, keeps what matters
3. Fully Connected → Flattens features into meaning
4. Output → Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically — edges → shapes → objects
Pixels to predictions. That's it. 👇
#DeepLearning #CNN #ComputerVision #AI
/channel/CodeProgrammer
Horizon Lab 🔭 Джеймс Вебб знаходить галактики, яких не мало б існувати за нашими моделями. Hubble бачить зірки, що вибухнули мільярди років тому. Пишемо про це щодня — українською, на основі наукових публікацій.
👉 http://t.me/horizonlab_space
Rocket.new lets you build a full website using prompts with their vibe solutioning platform 🧠⚡️
You describe it, it does the work.
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🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
➡️ /channel/CodeProgrammer
🚀 Top 25 Machine Learning Architecture Questions (Every ML Engineer Should Know)
Machine Learning isn’t just about training models it’s about designing systems that scale, perform, and survive production.
If you’re preparing for ML interviews, system design rounds, or real-world MLOps work, these are the most important ML Architecture questions you should be comfortable answering
🧠 Core ML Architecture Concepts
1️⃣ What is Machine Learning architecture and why does it matter?
2️⃣ Batch inference vs Real-time inference
3️⃣ What is model serving and common tools used
4️⃣ Data drift: what it is and how to handle it
5️⃣ Feature stores and their role in ML systems
6️⃣ What is MLOps and why it’s critical
⚙️ Training, Optimization & Pipelines
7️⃣ Training vs fine-tuning
8️⃣ Regularization techniques (L1, L2, Dropout, Early stopping)
9️⃣ Model versioning in production
🔟 ML pipelines and workflow automation
1️⃣1️⃣ CI/CD for ML systems
🗄 Data, Embeddings & Databases
1️⃣2️⃣ Choosing the right database for ML
1️⃣3️⃣ What are embeddings and why they’re powerful
1️⃣4️⃣ Handling sensitive data (GDPR, HIPAA, security)
📊 Monitoring, Explainability & Scaling
1️⃣5️⃣ Monitoring tools for ML models
1️⃣6️⃣ Explainability vs Interpretability
1️⃣7️⃣ Horizontal vs Vertical scaling
1️⃣8️⃣ Ensuring reproducibility in ML
1️⃣9️⃣ Factors affecting ML latency
🚢 Deployment & Production Strategies
2️⃣0️⃣ Why Docker/containerization matters
2️⃣1️⃣ GPU-accelerated deployment — when & why
2️⃣2️⃣ A/B testing in ML systems
2️⃣3️⃣ Multi-model deployment strategies
2️⃣4️⃣ Model rollback strategies
2️⃣5️⃣ Designing ML architectures for scalability
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
/channel/CodeProgrammer || Share 🌐 and Like 👍
🎁 23 Years of SPOTO – Claim Your Free IT Certs Prep Kit!
🔥Whether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
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・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4lk4m3c
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・IT Exams Skill Test: https://bit.ly/4sDvi0b
・Free AI material and support tools: https://bit.ly/46TpsQ8
・Free Cloud Study Guide: https://bit.ly/4lk3dIS
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Enter the Draw 👉: https://bit.ly/3NwkceD
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/Cnc5M5353oSBo3savBl397
💬 Want exam help? Chat with an admin now!
wa.link/rozuuw
⏰Last Chance – Get It Before It’s Gone!
Why pay $20 for each AI when you can access 90+ AI tools for the price of a single subscription?
The ultimate "Swiss Army Knife" of the AI world!
Why it’s a game-changer:
✅ All the top models in one place: ChatGPT-4o, Midjourney, Claude 3.5, Gemini, Nano Banana 2, and more.
✅ Convenience: Work via your browser or directly through the Telegram bot.
✅ No limits: Runs smoothly without a VPN, with flexible payment options.
Why you can trust it:
👥 Community: 700,000+ users on Telegram.
🧑🎓 Free Academy: Video tutorials included (perfect even for beginners).
🎥 Expert Content: Dedicated YouTube channel with deep dives.
Stop collecting subscriptions. Switch to the unified standard for AI access.
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ /channel/addlist/8_rRW2scgfRhOTc0
✅ /channel/Codeprogrammer
ML Engineer, LLM Engineer, take note: TorchCode
A platform with practice tasks for basic implementations in PyTorch and questions on Transformer, which are often encountered in interviews.
→ Gathers in 39 structured tasks typical for #ML #interviews - implementations of operators, modules, and architectures in #PyTorch.
→ Provides auto-checking, gradient checking, time measurement, and instant feedback, so that the practice more closely resembles #LeetCode for interviews.
→ Built on the basis of Jupyter Notebook, while supporting one-click reset, hints, reference solutions, and progress tracking.
→ Covers such frequent topics as ReLU, Softmax, LayerNorm, Attention, RoPE, Flash Attention, #LoRA, $MoE, and others.
→ Supports online mode via Hugging Face Spaces, opening individual tasks in #Google #Colab, and local launch via #Docker.
👉 https://github.com/duoan/TorchCode
Python Cheat Sheet: Beginner to Expert Guide
This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.
Read: https://www.almabetter.com/bytes/cheat-sheet/python
/channel/DataScience4 ✉️
This cheat sheet—part of our Complete Guide to #NumPy, #pandas, and #DataVisualization—offers a handy reference for essential pandas commands, focused on efficient #datamanipulation and analysis. Using examples from the Fortune 500 Companies #Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
/channel/CodeProgrammer
Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
/channel/CodeProgrammer 🌺
🤖 Python libraries for AI agents — what to study
If you want to develop AI agents in Python, it's important to understand the order of studying libraries.
Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas.
The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.
The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.
LangChain — simple agents, tools, and memory
github.com/langchain-ai/langchain
CrewAI — multi-agent systems with roles
github.com/joaomdmoura/crewAI
SmolAgents — lightweight agents for quick experiments
github.com/huggingface/smolagents
LangGraph — orchestration and stateful workflow
github.com/langchain-ai/langgraph
LlamaIndex — RAG and knowledge-agents
github.com/run-llama/llama_index
Semantic Kernel — AI workflow and plugins
github.com/microsoft/semantic-kernel
AutoGen — autonomous multi-agent systems
github.com/microsoft/autogen
DSPy — optimizing LLM pipelines
github.com/stanfordnlp/dspy
A2A — protocol for interaction between agents
github.com/a2aproject/A2A
/channel/CodeProgrammer 🌟
CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️
Two architectures. One goal: identify the cat. But they see things differently:
🧠 CNN (Convolutional Neural Network)
· Scans the image with filters
· Detects local patterns first (edges → textures → shapes)
· Builds understanding layer by layer
🔄 Vision Transformer (ViT)
· Splits image into patches (like words in a sentence)
· Detects global patterns from the start
· Sees the whole picture using attention mechanisms
Same input. Same output. Different journey.
CNNs think locally and build up.
Transformers think globally from the get-go.
Which one wins? Depends on the task — but both are shaping the future of how machines see.
/channel/CodeProgrammer
🚀 𝐓𝐎𝐏 𝐑𝐀𝐆 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 𝐐𝐔𝐄𝐒𝐓𝐈𝐎𝐍𝐒 𝐀𝐍𝐃 𝐀𝐍𝐒𝐖𝐄𝐑𝐒
🔹 Advanced #RAG engineering concepts
• Multi-stage retrieval pipelines
• Agentic RAG vs classical RAG
• Latency optimization
• Security risks in enterprise RAG systems
• Monitoring and debugging production RAG systems
📄 𝐓𝐡𝐞 𝐏𝐃𝐅 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐬 𝟒𝟎 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐫 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐛𝐨𝐭𝐡 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐚𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠.
/channel/CodeProgrammer
Python Tip: Operator Overloading
This is a very important concept in Python.
Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action.
Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.
🛫 ML Roadmap 2026 — a comprehensive guide to entering ML, LLM, and MLOps
A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.
Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
▶️ Foundations and tools;
▶️ Classic ML;
▶️ LLM and agents;
▶️ Engineering and MLOps;
▶️ Interview preparation.
➡️ GitHub link:
https://github.com/loganthorneloe/ml-roadmap
tags: #ml #llm
➡ /channel/CodeProgrammer
👨🏻💻 6 Free Python Certifications >>>
Python for Beginners -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/
Programming with Python 3. X
https://www.simplilearn.com/free-python-programming-course-skillup
Advanced Python -
https://www.codecademy.com/learn/learn-advanced-python
AI Python for Beginners -
https://www.deeplearning.ai/short-courses/ai-python-for-beginners/
Python Libraries for Data Science -
https://www.simplilearn.com/learn-python-libraries-free-course-skillup
Data Analysis with Python -
https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-courseLearn more and practice more 👨🏻💻
/channel/CodeProgrammer
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Читать полностью…
Знайшов цікавий сервіс для розробників — ApplicationHubs.
Це платформа, яка дозволяє запускати повноцінне Linux-середовище розробки у хмарі. Можна створити свій Dev Hub, підключитися через SSH, VSCode Remote або JetBrains Gateway і працювати як на звичайному комп'ютері — тільки без налаштування локального середовища.
Підтримуються Docker-проєкти, будь-які мови та фреймворки.
По суті це персональна cloud development machine, яку можна запустити за кілька секунд.
Зараз відкрито ранній доступ (early access).
👉 https://applicationhubs.com
The Ultimate 2026 Python Learning Roadmap: From Beginner to Expert
Start learning #Python in 2026 with a clear, structured #roadmap that takes you from beginner to expert. Build real-world skills through hands-on projects, master essential libraries, and prepare for in-demand careers in data science, web development, and #AI
Start: https://www.coursera.org/resources/python-learning-roadmap
🗂 A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
➡️ Link to the course
tags: #Python #DataScience #DeepLearning #AI
🧠 Python libraries for AI agents - complexity of learning 🔥
🟢 Easy
• LangChain
• tool calling
• agent memory
• simple agents
• CrewAI
• agents with roles
• collaboration of several agents
• SmolAgents
• lightweight agents
• quick experiments
🟡 Medium
• LangGraph
• stateful workflow
• agent orchestration
• LlamaIndex
• RAG pipelines
• data indexing
• knowledge agents
• OpenAI Agents SDK
• tool integrations
• agent workflows
• Strands
• agent orchestration
• task coordination
• Semantic Kernel
• skills / plugins
• AI process orchestration
• PydanticAI
• typed LLM applications
• structured agent workflows
• Langroid
• message exchange between agents
• interaction with tools
🔴 Difficult
• AutoGen
• multi-agent dialogues
• autonomous agent cooperation
• DSPy
• programmable prompting
• optimization of LLM pipelines
• A2A
• agent-to-agent protocol
• distributed agent systems
/channel/CodeProgrammer ✅
Pandas cheat sheet
Use the following Pandas cheat sheet to quickly reference some of the most common operations you might perform with the Pandas library.
More: https://www.coursera.org/resources/pandas-cheat-sheet
Matplotlib Cheat Sheet (Basics to Advanced)
Learn key Matplotlib functions with our Matplotlib cheat sheet. Includes examples, advanced customizations and comparison with Seaborn for better visualizations
Matplotlib is a versatile library in Python used for data visualization. Matplotlib enables the creation of static, interactive, and animated visualizations in Python. It is highly customizable and integrates well with libraries like Pandas and NumPy. Its pyplot module simplifies the process of creating plots similar to MATLAB. This Matplotlib cheat sheet provides an overview of the essential functions, features, and tools available in Matplotlib, along with comparisons to Seaborn where relevant.
Read: https://www.almabetter.com/bytes/cheat-sheet/matplotlib
/channel/CodeProgrammer
10 GitHub Repositories to Master System Design
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models.
What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking.
The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community.
In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale.
⚡️ MIT has released a full course on Deep Learning - for free
MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) — one of the most relevant and practical university courses on modern deep learning.
It includes full-fledged lectures at a top-university level, available for free.
What's in the course
- Fundamentals of deep learning and architectures
- Transformers and modern models
- Generative AI
- Self-supervised learning
- Scaling laws
- Diffusion and generative models
- RL and reinforcement learning
- Practical analyses of modern approaches
The lectures are led by MIT professors and researchers working with cutting-edge technologies.
Why it's valuable
This is not a basic course for beginners.
This is material at the level of:
- ML engineers
- researchers
- developers of AI systems
The course reflects the current state of the industry and explains how people who create modern models think.
It's perfect if you:
- already know Python and the basics of ML
- want to transition to Deep Learning
- work with LLMs / AI
- want a systematic understanding instead of individual tutorials
If you want FAANG / Research-level knowledge - learn from MIT.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
/channel/CodeProgrammer ✅