68382
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
🔥 Precision-Recall plot: Clearly explained
🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.
🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.
The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.
🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.
💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.
🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.
📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.
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ROC Plot: Clearly explained 🔥
💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).
🤔 Specificity and Sensitivity
The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.
Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.
🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.
😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).
A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.
📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.
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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 PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
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https://github.com/Ramakm/AI-ML-Book-References
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📰 Awesome Open Source AI 2026 — A comprehensive collection of current open-source AI projects 🤖
This repository consolidates significant resources in a single location, including frameworks, training tools, inference utilities, RAG solutions, agents, and more. The content is organized into distinct categories to facilitate efficient navigation and resource identification for specific tasks. 📂
Repo: https://github.com/alvinreal/awesome-opensource-ai
Tags: #github #useful ✔️
📚 Notes and Important Formulas ⬅️ "Matrices, Linear Algebra, and Probability"
👨🏻💻 This booklet serves as an essential resource for individuals initiating their studies in data science. It consolidates comprehensive information on matrices, linear algebra, and probability, thereby eliminating the necessity of consulting multiple sources.
✏️ The document encompasses nearly all pertinent formulas and key concepts. It addresses foundational topics such as determinants and matrix inverses, as well as advanced subjects including eigenvalues, eigenvectors, Singular Value Decomposition (SVD), and probability distributions.
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✔️ 10 Books to Understand How Large Language Models Function (2026)
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖
🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning
Both code and weights are available under the MIT license on HuggingFace.
👉 Key details:
• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture
Models:
🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP
⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context
Engineering highlights:
• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality
🌍 Trained on 14 languages (optimized for English and Russian)
Use cases:
• chatbots
• AI assistants
• copilots
• internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
📌 A comprehensive masterclass on Claude Code is available via this repository: https://github.com/luongnv89/claude-howto.
This resource provides a detailed visual and practical guide for one of the most powerful tools for developers. The repository includes:
• Step-by-step learning paths covering basic commands (/init, /plan) to advanced features such as MCP, hooks, and agents, achievable in approximately 11–13 hours. 📚
• An extensive library of custom commands designed for real-world tasks.
• Ready-made memory templates for both individual and team workflows.
• Instructions and scripts for:
- Automated code review.
- Style and standards compliance checks.
- API documentation generation.
• Automation cycles enabling autonomous operation of Claude without direct user intervention. ⚙️
• Integration with external tools, including GitHub and various APIs, presented with step-by-step guidance.
• Diagrams and charts to facilitate understanding, suitable for beginners. 📊
• Examples for configuring highly specialized sub-agents.
• Dedicated learning scripts, such as tools for generating educational books and materials to master specific topics efficiently.
Access the full guide here: https://github.com/luongnv89/claude-howto
The 10 Most Valuable AI Learning Repositories on GitHub 👇
I pulled the top 10 repos where Jupyter is the main language
Filtered for the best educational resources
Here's what's worth your time :
1. microsoft/generative-ai-for-beginners ⭐ 105,577 21
lessons covering the full GenAI stack From prompting basics to production deployment Built by Microsoft's AI education team
🔗 https://lnkd.in/diW9Cca6
2. rasbt/LLMs-from-scratch ⭐ 83,714
Build GPT-like models from zero No hand-waving, pure implementation Companion to Sebastian Raschka's book
🔗 https://lnkd.in/d3cq5diH
3. microsoft/ai-agents-for-beginners ⭐ 49,333
Complete course on agentic systems Covers planning, tools, memory, multi-agent Released 3 months ago, already essential
🔗 https://lnkd.in/e-a2gqSv
4. microsoft/ML-For-Beginners ⭐ 83,279
12 weeks of classical ML fundamentals 26 lessons, 52 quizzes, full curriculum Still relevant despite the LLM hype
🔗 https://lnkd.in/e7S8yDbS
5. openai/openai-cookbook ⭐ 71,106
Official OpenAI examples and guides Real production patterns, not toys Updated constantly with new features
🔗 https://lnkd.in/dtMbuMGk
6. jackfrued/Python-100-Days ⭐ 177,958
Most-starred educational repo on GitHub 100 days from Python beginner to advanced Covers web dev, data science, automation
🔗 https://lnkd.in/duWVtn4i
7. pathwaycom/llm-app ⭐ 54,583
Production RAG templates you can deploy Real-time data pipelines, not static demos Enterprise search with live updates
🔗 https://lnkd.in/daUFK9Nd
8. jakevdp/PythonDataScienceHandbook ⭐ 46,574
Entire data science handbook as Jupyter notebooks NumPy, Pandas, Matplotlib, Scikit-Learn Free alternative to $60 textbook
🔗 https://lnkd.in/db8HP7vT
9. CompVis/stable-diffusion ⭐ 72,246
Original Stable Diffusion implementation Understand how text-to-image actually works Foundation for SDXL, Midjourney competitors
🔗 https://lnkd.in/dEya2Rb5
10. facebookresearch/segment-anything ⭐ 53,250
Meta's SAM model for computer vision Promptable segmentation in images and videos Powers modern AI video editing tools
🔗 https://lnkd.in/dKvjk6Yb
A new open-source Python library titled "Fli" has been released, offering direct access to Google Flights. This library circumvents the web interface by interfacing directly with a reverse-engineered API to deliver rapid and structured results. The project is 100% open-source.
100% open-source.
🚀 Fine-Tuning Large Language Models for Domain-Specific Tasks
Fine-tuning Large Language Models is the process by which generic LLMs are transformed into domain-specific experts. This procedure updates model weights using task-specific labeled data, rather than relying solely on prompting or retrieval mechanisms. This approach is particularly effective when language patterns remain stable and consistent outputs are required.
👉 Core Concept
A pre-trained LLM acquires general language capabilities. Fine-tuning instructs the model on how language functions within specific domains, such as healthcare, finance, legal services, or internal enterprise workflows.
👉 Practical Implementation
A customer support model is trained on thousands of instruction-response pairs. For example:
Input: Refund request for a delayed shipment
Output: A policy-compliant response including an apology, procedural steps, and a resolution.
Following fine-tuning, the model generates consistent, policy-aligned answers with lower latency compared to Retrieval-Augmented Generation (RAG).
👉 Significance of Parameter-Efficient Fine-Tuning
Techniques such as LoRA and QLoRA train only small adapter layers while keeping the base model frozen. This methodology reduces GPU memory consumption, accelerates training, and enables the fine-tuning of large models on hardware with limited resources.
👉 Appropriate Use Cases for Fine-Tuning
- Recurring domain-specific language
- Structured outputs, including classifications, summaries, or templates
- Stable knowledge bases that do not undergo daily changes
- Latency-sensitive systems where retrieval introduces overhead
Typical Production Stack
- Models: LLaMA or Mistral
- Frameworks: PyTorch with Hugging Face and PEFT
- Optimization: DeepSpeed or Accelerate
- Deployment: FastAPI, Docker, and cloud GPUs
💡 Fine-tuning enhances accuracy, consistency, and cost efficiency when applied to suitable problems.
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
/channel/CodeProgrammer ✅
On GitHub, a repository has been curated comprising over 500 valuable services designed for daily tasks. 📂🛠️
The collection includes projects compatible with various operating systems, smartphones, web browsers, and torrent clients, alongside tools for productivity, software development, design, and content management. 🖥️📱🎨
https://github.com/Furthir/awesome-useful-projects?tab=readme-ov-file#creative 🔗
Confused between ML, NLP, Generative, and other AI models? 🤔
Here’s a quick breakdown of the 6 most important types of AI models you must understand in 2026👇
1. Machine Learning Models 🤖
They learn from labeled and unlabeled data to classify, predict, and detect patterns. Think decision trees, SVMs, and XGBoost.
2. Deep Learning Models 🧠
Neural networks built for unstructured data like images, audio, and text. Includes CNNs, RNNs, Transformers, and GANs.
3. NLP Models 💬
Focused on understanding and generating human language - used in chatbots, summarizers, and assistants like GPT and BERT.
4. Generative Models ✨
These models create, from text to images to music. Powered by models like GPT-4, DALL·E, and StyleGAN.
5. Hybrid Models 🔗
Combine the best of rule-based and neural AI. Perfect for use cases needing both reasoning and context awareness (e.g., RAG pipelines).
6. Computer Vision Models 👁
Built for images and videos. Used in object detection, facial recognition, and medical scans - powered by models like YOLO and ResNet.
Each AI model has its strengths and knowing which one fits your use case is half the battle. Save this guide as your cheat sheet! 📝✅
Most people learn Python in random order. No wonder they feel stuck.
This roadmap fixes that.
Here are the 5 layers every data professional must master, in order:
𝟭. 𝗖𝗼𝗿𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 (𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻)
Variables, loops, functions, error handling, collections.
Do not skip this. Everything else breaks without it.
𝟮. 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
Pandas, NumPy, file handling, SQL integration, data cleaning.
This is where your actual job begins.
𝟯. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀
Matplotlib, Seaborn, EDA, statistical functions, hypothesis testing.
Can you turn raw data into a decision? This layer teaches you how.
𝟰. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗟
Scikit-Learn, clustering, feature engineering, big data tools.
This is what gets you promoted.
𝟱. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
Git, virtual environments, unit testing, workflow scheduling.
This is what separates professionals from beginners.
The mistake most people make, they jump straight to ML without nailing the foundation.
You cannot build insights on broken code.
Master the layers. In order. With real data.
Save this roadmap and share it with someone who needs direction.
Where are you on this right now?
♻️ Repost to help someone learning Python the right way
/channel/CodeProgrammer ✅
📌 Claude Code: A comprehensive collection of resources for professional development.
This compilation includes videos, repositories, documentation, and books. The content is curated to ensure relevance and eliminate unnecessary information.
🗂 Repositories
Claude Code (Official)
https://github.com/anthropics/claude-code
Claude Cookbooks
https://github.com/anthropics/claude-cookbooks
Ultimate Guide to Claude Code
https://github.com/FlorianBruhinux/claude-code-ultimate-guide
Collection of the Best Claude Plugins
https://github.com/quemsah/awesome-claude-plugins
Best Repositories on Claude Code
https://mejba.me/locale/en?next=%2Fblog%2Fbest-github-repos-claude-code
📚 Guides and Documentation
Overview of Claude Code Documentation
https://code.claude.com/docs/en/overview
Claude Code Handbook (freeCodeCamp)
https://freecodecamp.org/news/claude-code-handbook/
A Complete Guide to Claude Code (2026)
https://claude-world.com/articles/claude-code-complete-guide-2026/
A Practical Guide to Claude Code
https://evakeiffenheim.substack.com/p/a-clear-guide-to-claude-code-for
A Beginner's Guide to Claude Code
https://nxcode.io/resources/news/claude-code-tutorial-beginners-guide-2026
🎥 Videos
A Complete Guide to Claude Code for Beginners (2026)
https://youtube.com/watch?v=qYqIhX9hTQk
A Full Course on Claude Code: Creation and Monetization (4 Hours)
https://youtube.com/watch?v=QoQBzR1NlqI
Master Claude Code in 30 Minutes
https://youtube.com/watch?v=6eBSHbLKuN0
Master 95% of Claude Code Skills in 28 Minutes
https://youtube.com/watch?v=zKBPwDpBfhs
A Playlist on Claude Code (Beginner to Advanced)
https://youtube.com/playlist?list=PL4HikwTaYE0ETMaJqnNvm_2I3NEbexMDZ
Top Six Tips for Effective Work with Claude Code
https://youtube.com/watch?v=WwdlYp5fuxY
📖 Books
Mastering Claude AI: A Practical Journey
https://amazon.com/Mastering-Claude-AI-Practical-Journey/dp/B0FLJEY8BD
AI Engineering by Chip Huyen
https://amazon.com/AI-Engineering-Building-Applications-Foundation/dp/B0F3ZZTKG5
Claude Code Lab: Production AI Applications
https://books.google.com/books/about/Claude_Code_Lab.html?id=EOng0QEACAAJ
It is recommended to save this resource for future reference. Sharing this compilation with colleagues may facilitate their professional development in Claude Code.
📚 CS50's Introduction to Programming with Python
#Development #Python #Free #Harvard_University #Others
📝 An introduction to Python programming, a popular language for general-purpose programming, data research, web development, and other applications.
⏱ Duration: 80 h
🎓 Features: OthersHarvard University • English • Beginner • Development,Python
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📝 12 Essential Articles for Data Scientists
🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
/channel/CodeProgrammer 🌟
Hyper-Extract 🚀
It uses LLM to convert unstructured text into structured data. You can input a large amount of "dirty" text, and it will automatically extract the structure and generate a knowledge graph using LLM. 🧠📊
It includes a CLI utility that can be launched with a single command, as well as more than 80 ready-made domain templates (finance, healthcare, law, etc.) - there's no need to write your own prompts. ⚙️📝
https://github.com/yifanfeng97/Hyper-Extract 🔗
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Data Analysis Bootcamp: Master Data Science Skills
Learn Data Analysis With Python, Jupyter, Pandas, Dropna - Learn Data Cleaning, Visualization, and Modeling...
🏷 Category: development
🌍 Language: English (US)
👥 Students: 32,910 students
⭐️ Rating: 4.5/5.0 (393 reviews)
🏃♂️ Enrollments Left: 87
⏳ Expires In: 0D:30H:30M
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Python Complete Course For Python Beginners
Python Complete Course For Python Beginners.Learn Python From Beginner To Advanced Level...
🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 345,060 students
⭐️ Rating: 4.3/5.0 (5,218 reviews)
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⏳ Expires In: 0D:30H:30M
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