Machine Learning & Artificial Intelligence | Data Science Free Courses
Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
Show more📈 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 66 752 subscribers, ranking 2 450 in the Education category and 436 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 66 752 subscribers.
According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 534 over the last 30 days and by 42 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.75%. Within the first 24 hours after publication, content typically collects 0.79% reactions from the total number of subscribers.
- Post reach: On average, each post receives 502 views. Within the first day, a publication typically gains 524 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- 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 25 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.
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| Date | Subscriber Growth | Mentions | Channels | |
| 26 June | +4 | |||
| 25 June | +35 | |||
| 24 June | +50 | |||
| 23 June | +37 | |||
| 22 June | +11 | |||
| 21 June | +16 | |||
| 20 June | +17 | |||
| 19 June | +10 | |||
| 18 June | +22 | |||
| 17 June | +9 | |||
| 16 June | +26 | |||
| 15 June | +34 | |||
| 14 June | +55 | |||
| 13 June | +52 | |||
| 12 June | +34 | |||
| 11 June | +20 | |||
| 10 June | +28 | |||
| 09 June | +25 | |||
| 08 June | +23 | |||
| 07 June | +29 | |||
| 06 June | +25 | |||
| 05 June | +26 | |||
| 04 June | +36 | |||
| 03 June | +37 | |||
| 02 June | +37 | |||
| 01 June | +29 |
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| 19 | ✅ 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
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