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Machine Learning & Artificial Intelligence | Data Science Free Courses

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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun

Network:Data Analytics Malaysia418 Education2 398...

📈 Analytical overview of Telegram channel Machine Learning & Artificial Intelligence | Data Science Free Courses

Channel Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) in the English language segment is an active participant. Currently, the community unites 68 675 subscribers, ranking 2 398 in the Education category and 418 in the Malaysia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 675 subscribers.

According to the latest data from 26 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 257 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.32%. Within the first 24 hours after publication, content typically collects 0.85% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 964 views. Within the first day, a publication typically gains 581 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
Thematic interests: Content is focused on key topics such as sellerflash, waybienad, pricing, buybox, buyer.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun”

Thanks to the high frequency of updates (latest data received on 27 October, 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.

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68 675

Subscribers

+824 hours

+97 days

+25730 days

2 964

Post views

~ 58124 hours

~ 78148 hours

4.32%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

68 673

Skills for Data Scientists 👆

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𝗪𝗮𝗻𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗘𝘅𝗰𝗲𝗹 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟳 𝗱𝗮𝘆𝘀? 📊 Here's a structured roadmap to help you go from beginner to pro in a week! Whether you're learning formulas, functions, or data visualization, this guide covers everything step by step. 𝐋𝐢𝐧𝐤👇 :- https://pdlink.in/43lzybE All The Best 💥

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Tech stack for Machine Learning in 2024: - ml workflow orchestrator: Kubeflow - experiment tracking: MLflow - data ingestion: Airbyte - job orchestrator: Apache Airflow - batch pipeline: Apache Spark - message queue for real-time streaming: Apache Kafka - feature engineering: Scikit-learn - model selection and training: Pytorch - hyperparameter tuning: Ray Tune - model evaluation: Weights & Biases - model monitoring: Grafana - CI/CD: Github actions - model versioning: neptune - model serving: BentoML - web app framework: Flask - front-end: React - feature store: Qwak - Graph database: Neo4j - Vector database: ChromaDB - NoSQL database: MongoDB - In-memory data store: Redis ... What is your current ML tech stack?

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This post is for beginners who decided to learn Data Science. I want to tell you that becoming a data scientist is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations. There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below: Basic Statistics, Linear Algebra, calculus, probability Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science. Machine Learning - All of the above will be used here to implement machine learning concepts. Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc. This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order. You can use the below Sources to prepare your own roadmap: @free4unow_backup - some free courses from here @datasciencefun - check & search in this channel with #freecourses Data Science - https://365datascience.pxf.io/q4m66g Python - https://bit.ly/45rlWZE Kaggle - https://www.kaggle.com/learn

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𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊🚀 Want to master data analytics? Here are top free courses, books, and certifications to help you get started with Power BI, Tableau, Python, and Excel. 𝐋𝐢𝐧𝐤👇 https://pdlink.in/41Fx3PW All The Best 💥

68 673