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📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 72 399 obunachiga ega bo‘ldi.
31 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 363 ga, so‘nggi 24 soatda esa -19 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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Obunachilar
-1924 soatlar
-1267 kun
+36330 kun
Postlar arxiv
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🔅 Machine Learning for Red Team Hackers by Infosec
🌐 Author: Infosec Institute
🔰 Level: Intermediate
⏰ Duration: 3h 39m
🌀 Learn the various techniques used in hacking machine learning.📗 Topics: Ethical Hacking, Machine Learning, Red Teaming 📤 Join Artificial Intelligence and Machine Learning for more courses
72 399
🤗 HuggingFace is offering 9 AI courses for FREE!
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
1️⃣ LLM Course:
https://huggingface.co/learn/llm-course/chapter1/1
2️⃣ Agents Course:
https://huggingface.co/learn/agents-course/unit0/introduction
3️⃣ Deep Reinforcement Learning Course:
https://huggingface.co/learn/deep-rl-course/unit0/introduction
4️⃣ Open-Source AI Cookbook:
https://huggingface.co/learn/cookbook/index
5️⃣ Machine Learning for Games Course
https://huggingface.co/learn/ml-games-course/unit0/introduction
6️⃣ Hugging Face Audio course:
https://huggingface.co/learn/audio-course/chapter0/introduction
7️⃣ Vision Course:
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
8️⃣ Machine Learning for 3D Course:
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
9️⃣ Hugging Face Diffusion Models Course:
https://huggingface.co/learn/diffusion-course/unit0/1
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+3
🔥 Google has introduced InstructPipe , an AI editor for ML pipelines that works via text queries.
❔ What is InstructPipe?
InstructPipe is an AI assistant that transforms text commands into visual flowcharts representing machine learning pipelines.
The system uses two large language model (LLM) modules and a code interpreter to generate pseudocode and visualize it in a graph editor.
This is a low-code approach: you simply connect ready-made components (nodes) without writing code.
🌟 How does this work?
1️⃣ The user enters a text instruction describing the desired pipeline.
2️⃣ LLM modules process the instruction and generate the corresponding pseudocode.
3️⃣ The code interpreter converts pseudocode into a visual flowchart that you can edit and customize.
✔️ Benefits of InstructPipe
🟡 Accessibility: Allows newcomers to programming to create complex ML pipelines without having to write code.
🟡 Flexibility: Accepts text description in any form, no strict format.
🟡 Lower barrier to entry: Simplifies the process of learning and prototyping ml projects.
🔜 Read more
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🔗 Top 5 machine learning projects:
1. Predicting House Prices: Build a machine learning model that predicts house prices based on features such as location, size, number of bedrooms, etc. This project will help you understand regression techniques and feature engineering.
2. Image Classification: Create a model that can classify images into different categories such as cats vs. dogs, fruits, or handwritten digits. This project will introduce you to convolutional neural networks (CNNs) and image processing.
3. Sentiment Analysis: Develop a sentiment analysis model that can classify text data as positive, negative, or neutral. This project will help you learn natural language processing techniques and text classification algorithms.
4. Credit Card Fraud Detection: Build a model that can detect fraudulent credit card transactions based on transaction data. This project will help you understand anomaly detection techniques and imbalanced classification problems.
5. Recommendation System: Create a recommendation system that suggests products or movies to users based on their preferences and behavior. This project will introduce you to collaborative filtering and recommendation algorithms.
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👨🏻💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
✅ Why should we use it?
🔢 For learning: If you're looking to learn algorithms in action, this is great.
🔢 For practice: You can take the codes, run them, and modify them to better understand.
🔢 For projects : You can even use the codes here in real-life or academic projects.
🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
┌ 🏳️🌈 The Algorithms - Python
└ 🐱 GitHub-Repos
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Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
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📂 Full description
Theres an increased demand to integrate AI and machine learning workflows into many different business sectors. This is especially true in todays unique and constantly evolving global healthcare landscape.In this course, instructor Wuraola Oyewusi provides an overview of how AI and machine learning can optimize healthcare processes, data analysis, health outcomes, and more. Along the way, gather insights drawn from real-world examples to address complex privacy and ethical considerations in the industry. Wuraola also shows you how to utilize machine learning for tabular healthcare datasets using a Google Colab Notebook, including clinical records, classification, predictions, regression, clustering, and localization.
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🔅 Machine Learning Fundamentals for Healthcare
🌐 Author: Wuraola Oyewusi
🔰 Level: Beginner
⏰ Duration: 1h 36m
🌀 Get an introduction to the fundamentals of machine learning and AI in this course designed for healthcare professionals.📗 Topics: Healthcare Information Technology, Machine Learning 📤 Join Artificial Intelligence and Machine Learning for more courses
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