Available now! Telegram Research 2025 โ the year's key insights 
Artificial Intelligence
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๐ฐ Machine Learning & Artificial Intelligence Free Resources ๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Show more๐ Analytical overview of Telegram channel Artificial Intelligence
Channel Artificial Intelligence (@machinelearning_deeplearning) in the English language segment is an active participant. Currently, the community unites 53 161 subscribers, ranking 3 256 in the Education category and 7 041 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 53 161 subscribers.
According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 045 over the last 30 days and by 38 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.69%. Within the first 24 hours after publication, content typically collects 1.68% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 022 views. Within the first day, a publication typically gains 892 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, classification, layer, pattern, chatbot.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โ๐ฐ Machine Learning & Artificial Intelligence Free Resources
๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_dataโ
Thanks to the high frequency of updates (latest data received on 10 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.
53 161
Subscribers
+3824 hours
+1977 days
+1 04530 days
Posts Archive
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8 FREE AI Courses by Google ๐๐ Learn, Grow, and Succeed
1. Introduction to Generative AI
โ An introductory course to explain what generative AI is.
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Underrated Telegram Channel for Data Analysts ๐๐
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Here, you will get free tutorials to learn SQL, Python, Power BI, Excel and many more
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To automate your daily tasks using ChatGPT, you can follow these steps:
1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.
2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.
3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.
4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.
5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.
6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.
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Essential Data Analysis Techniques Every Analyst Should Know
1. Descriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.
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3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.
4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.
5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.
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8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.
9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.
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"I am an AI Tools & ChatGPT Expert, and my salary package is 42 LPA."
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See guys, learning how to use a few AI tools won't magically get you a 42 LPA job. Selling courses isnโt wrong, but selling them by giving false hopes is. Just because someone tells you that learning how to use a few AI tools will instantly land you a high-paying job doesnโt make it true.
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If anyone actually finds this 42 LPA AI Tools & ChatGPT Expert job, please let me know. Iโll also update my resume! ๐คฃ
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Master AI in 2025 โ A Quick Roadmap ๐
AI can be overwhelming, but following a structured path makes it easier. Hereโs the roadmap:
1. Build Strong Foundations Learn Python, data structures, linear algebra, statistics & version control before diving into AI.
2. Work with Data Clean, preprocess & visualize datasets using Pandas, Seaborn, and Matplotlib for hands-on experience.
3. Master Machine Learning Understand supervised & unsupervised learning, regression, decision trees & implement models with Scikit-Learn.
4. Explore Deep Learning Learn neural networks, CNNs, RNNs, and Transformers using TensorFlow & PyTorch for AI applications.
5. Choose an AI Specialization Focus on NLP, computer vision, reinforcement learning, or AI in business and healthcare.
6. Learn Large Language Models (LLMs) Work with GPT, LLaMA, fine-tuning, Retrieval-Augmented Generation (RAG), and AI APIs.
7. Master AI Deployment & MLOps Deploy models using Flask, FastAPI, Docker, Kubernetes, and automate pipelines.
