Artificial Intelligence

Open in Telegram

🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM

Network:Zero To Mastery India4 344 Technologies & Applications1 724...

📈 Analytical overview of Telegram channel Artificial Intelligence

Channel Artificial Intelligence (@artificial_intelligence_com) in the English language segment is an active participant. Currently, the community unites 72 399 subscribers, ranking 1 724 in the Technologies & Applications category and 4 344 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 72 399 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 363 over the last 30 days and by -19 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.53%. Within the first 24 hours after publication, content typically collects 1.94% reactions from the total number of subscribers.
Post reach: On average, each post receives 4 727 views. Within the first day, a publication typically gains 1 407 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 13.
Thematic interests: Content is focused on key topics such as learning, linkedin, linux, udemy, 040k|.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM”

Thanks to the high frequency of updates (latest data received on 01 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

72 399

Subscribers

-1924 hours

-1267 days

+36330 days

4 727

Post views

~ 1 40724 hours

~ 1 90648 hours

6.53%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

72 399

🔅 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

72 399

72 399

🔥 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

72 399

🔗 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.

72 399

👨🏻‍💻 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

72 399

🔗 Types of Machine Learning

72 399

72 399

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.

72 399

📦 Exercise Files

72 399

📱Artificial Intelligence and Machine Learning 📱Machine Learning Fundamentals for Healthcare

72 399

📂 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.

72 399

🔅 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

72 399

72 399

72 399

72 399

fine_tuning_llms_with_hugging_face_partial_code.py0.02 KB

72 399

🔅 07 - FineTuning LLMs with Hugging Face Step 4

72 399

🔅 06 - FineTuning LLMs with Hugging Face Step 6

72 399

🔅 05 - FineTuning LLMs with Hugging Face Step 2