Artificial Intelligence

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

Network:Zero To Mastery India4 785 Technologies & Applications1 849...

📈 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 70 419 subscribers, ranking 1 849 in the Technologies & Applications category and 4 785 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 70 419 subscribers.

According to the latest data from 13 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 217 over the last 30 days and by 69 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.35%. Within the first 24 hours after publication, content typically collects 2.09% reactions from the total number of subscribers.
Post reach: On average, each post receives 5 179 views. Within the first day, a publication typically gains 1 474 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 10.
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 14 June, 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.

70 419

Subscribers

+6924 hours

+2577 days

+1 21730 days

5 179

Post views

~ 1 47424 hours

~ 1 98348 hours

7.35%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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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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🔗 Types of Machine Learning

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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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📦 Exercise Files

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

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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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fine_tuning_llms_with_hugging_face_partial_code.py0.02 KB

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🔅 07 - FineTuning LLMs with Hugging Face Step 4

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🔅 06 - FineTuning LLMs with Hugging Face Step 6

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🔅 05 - FineTuning LLMs with Hugging Face Step 2

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🔅 04 - FineTuning LLMs with Hugging Face Step 4

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🔅 03 - FineTuning LLMs with Hugging Face Step 7

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🔅 02 - FineTuning LLMs with Hugging Face Step 6

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🔅 01 - FineTuning LLMs with Hugging Face Step 5

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LLMs Implementation