AI and Machine Learning

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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

India3 005 Education1 547...

📈 Analytical overview of Telegram channel AI and Machine Learning

Channel AI and Machine Learning (@machine_learning_courses) in the English language segment is an active participant. Currently, the community unites 94 077 subscribers, ranking 1 547 in the Education category and 3 005 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 94 077 subscribers.

According to the latest data from 26 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 965 over the last 30 days and by 37 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.79%. Within the first 24 hours after publication, content typically collects 2.34% reactions from the total number of subscribers.
Post reach: On average, each post receives 6 384 views. Within the first day, a publication typically gains 2 203 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
Thematic interests: Content is focused on key topics such as learning, llm, linkedin, linux, udemy.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses”

Thanks to the high frequency of updates (latest data received on 27 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 Education category.

94 077

Subscribers

+3724 hours

+2267 days

+96530 days

6 384

Post views

~ 2 20324 hours

~ 2 91648 hours

6.79%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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01 - Introduction.zip34.75 MB

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🔅 AI for Beginners: Inside Large Language Models ⏲ 3 hours 📁 326 Lessons

📔 Understand how LLMs actually work under the hood from scratch with practical and fun lessons. No prior knowledge required!

🎙 Taught by: Scott Kerr 📤 Download All Courses

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🚀 TrajectoryCrafter (Moving-Camera Diffusion) is a new tool from Tencent that offers a new approach to redirecting camera trajectories in monochrome videos. How the model works: 🌟 Initialization : starts with an existing camera trajectory or even pure noise. This sets the initial state that the model will gradually improve. The model uses two types of input data simultaneously: rendered point clouds (3D representations of scenes) and source videos. 🌟 Diffusion process: The model learns to “clean up” random noise step by step, turning it into a sequence of trajectories. At each step, iterative refinement occurs — the model predicts what a more realistic trajectory should look like, based on given conditions (e.g., smoothness of motion, and consistency of the scene). Instead of using only videos taken from different angles, the authors created a training set by combining extensive monocular videos (with a regular camera) with limited but high-quality multi-view videos. This strategy is achieved using what is called “double reprojection”, which helps the model better adapt to different scenes. 🌟 Generating the final trajectory: After a series of iterations, when the noise is removed, a new camera trajectory is generated that meets the given conditions and has high quality visual dynamics. Installation :

git clone --recursive https://github.com/TrajectoryCrafter/TrajectoryCrafter.git
cd TrajectoryCrafter

🖥 Github 🟡 Article 🟡 Project 🟡 Demo 🟡 Video

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📱Artificial intelligence 📱AI Projects with Python, TensorFlow, and NLTK

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🔅 AI Projects with Python, TensorFlow, and NLTK 📝 Supercharge your technical know-how and start building AI projects using Python, TensorFlow, and NLTK. 🌐 Author: Dhhyey Desai 🔰 Level: Intermediate ⏰ Duration: 24m 📋 Topics: TensorFlow, Artificial Intelligence, NLTK 🔗 Join Artificial intelligence for more courses

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💡 20 Concepts In LLMs

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🔆 Random Forest explained

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📱Artificial intelligence 📱Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications

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🔅 Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications 📝 Discover the core concepts of successful AI applications using LLMs to achieve high levels of performance and accuracy. 🌐 Author: Guy Ernest 🔰 Level: Advanced ⏰ Duration: 1h 47m 📋 Topics: Retrieval-Augmented Generation, Large Language Models, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

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🔗 Master AI in 2026

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💡 9 AI Skills to Master in 2026 It’s the infrastructure behind how smart businesses run today. The gap between users and experts is closing fast. But the gap between curiosity and capability is getting wider. The difference comes down to skill, not just tools. These are the nine that matter most in 2026. Each one compounds the rest and turns AI from novelty into leverage. 1⃣ Prompt Engineering to ask better questions and get sharper answers 🔢 AI Workflow Automation to connect apps and remove repetitive work 🔢 AI Agents to build systems that act without human input 🔢 Retrieval-Augmented Generation (RAG) to give models access to your own data 🔢 Fine-Tuning and Custom GPTs to train models for your goals and tone 🔢 Multimodal AI to mix text, image, and audio in one workflow 🔢 AI Video Generation to turn ideas into content without editing tools 🔢 AI Tool Stacking to link platforms into a single automated system 🔢 LLM Evaluation and Management to measure accuracy, cost, and performance

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💡 13 Practical Steps For Creating an AI Agent

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📱Artificial intelligence 📱Natural Language Processing (NLP) on Amazon Bedrock

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🔅 Natural Language Processing (NLP) on Amazon Bedrock 📝 This course addresses natural language AI tasks using Bedrock's LLMs, Amazon Q capabilities, and SageMaker NLP models. 🌐 Author: Noah Gift 🔰 Level: Intermediate ⏰ Duration: 56m 📋 Topics: Amazon Bedrock, Large Language Models, Natural Language Processing 🔗 Join Artificial intelligence for more courses

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💡 The AI Universe This visual guide clearly illustrates the different layers and concepts within Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.

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Telegram: Launch @argo Ever wondered how to find all kinds of Telegram gems in seconds? TryArgo Search 🔍: one resource, endless channels, trending groups, fresh news, music, movies — all at your fingertips. What will you discover today? 🚀 👉 Explore now!!

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🔗 7 Advanced Retriever Architecture

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🔗 Machine Learning Cheat Sheet