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Machine Learning & Artificial Intelligence | Data Science Free Courses

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

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📈 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 68 183 subscribers, ranking 2 374 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 68 183 subscribers.

According to the latest data from 04 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 514 over the last 30 days and by 28 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 5.03%. Within the first 24 hours after publication, content typically collects 1.14% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 3 429 views. Within the first day, a publication typically gains 775 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
  • 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 05 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 Education category.

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68 183
Subscribers
+2824 hours
+1617 days
+51430 days
Posts Archive
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Data Science Mistakes Beginners Should Avoid ⚠️📉 1️⃣ Skipping the Basics • Jumping into ML without Python, Stats, or Pandas ✅ Build strong foundations in math, programming & EDA first 2️⃣ Not Understanding the Problem • Applying models blindly • Irrelevant features and metrics ✅ Always clarify business goals before coding 3️⃣ Treating Data Cleaning as Optional • Training on dirty/incomplete data ✅ Spend time on preprocessing — it’s 70% of real work 4️⃣ Using Complex Models Too Early • Overfitting small datasets • Ignoring simpler, interpretable models ✅ Start with baseline models (Logistic Regression, Decision Trees) 5️⃣ No Evaluation Strategy • Relying only on accuracy ✅ Use proper metrics (F1, AUC, MAE) based on problem type 6️⃣ Not Visualizing Data • Missed outliers and patterns ✅ Use Seaborn, Matplotlib, Plotly for EDA 7️⃣ Poor Feature Engineering • Feeding raw data into models ✅ Create meaningful features that boost performance 8️⃣ Ignoring Domain Knowledge • Features don’t align with real-world logic ✅ Talk to stakeholders or do research before modeling 9️⃣ No Practice with Real Datasets • Kaggle-only learning ✅ Work with messy, real-world data (open data portals, APIs) 🔟 Not Documenting or Sharing Work • No GitHub, no portfolio ✅ Document notebooks, write blogs, push projects online 💬 Tap ❤️ for more!

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