Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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网络:📈 Telegram 频道 Machine Learning 的分析概览
频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 41 884 名订阅者，在 技术与应用 类别中位列第 3 207，并在 叙利亚 地区排名第 211 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 41 884 名订阅者。
根据 26 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 467，过去 24 小时变化为 3，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 5.28%。内容发布后 24 小时内通常能获得 2.07% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 211 次浏览，首日通常累积 866 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 5。
- 主题关注点： 内容集中在 distance, insidead, gpu, learning, degree 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 27 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
41 884
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+324 小时
+527 天
+46730 天
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 26 十月 | +9 | |||
| 25 十月 | +10 | |||
| 24 十月 | +5 | |||
| 23 十月 | +4 | |||
| 22 十月 | +13 | |||
| 21 十月 | +11 | |||
| 20 十月 | +23 | |||
| 19 十月 | +2 | |||
| 18 十月 | +12 | |||
| 17 十月 | +15 | |||
| 16 十月 | +14 | |||
| 15 十月 | +6 | |||
| 14 十月 | +19 | |||
| 13 十月 | +19 | |||
| 12 十月 | +10 | |||
| 11 十月 | +12 | |||
| 10 十月 | +17 | |||
| 09 十月 | +27 | |||
| 08 十月 | +22 | |||
| 07 十月 | +17 | |||
| 06 十月 | +25 | |||
| 05 十月 | +18 | |||
| 04 十月 | +40 | |||
| 03 十月 | +13 | |||
| 02 十月 | +17 | |||
| 01 十月 | +27 |
频道帖子
The 25 Best Free Resources for Learning AI in 2026
Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.
Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
| 2 |  🔖 637 interactive modules on ML and AI
On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
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🫡 Quantization;
🫡 Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
⛓ Link to the tutorial
https://vizlearn.in/ | 810 |
| 3 |  🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup
If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.
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| 4 | Agentic AI is the future.
If you want to become god-level with AI agents, save these 10 GitHub repos:
1 Awesome LLM Apps
🔗 https://github.com/Shubhamsaboo/awesome-llm-apps
2 Generative AI for Beginners
🔗 https://github.com/microsoft/generative-ai-for-beginners
3 LLM Course
🔗 https://github.com/mlabonne/llm-course
4 Prompt Engineering Guide
🔗 https://github.com/dair-ai/Prompt-Engineering-Guide
5 AI Agents for Beginners
🔗 https://github.com/microsoft/ai-agents-for-beginners
6 AI Agents Projects
🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects
7 Hugging Face Agents Course
🔗 https://github.com/huggingface/agents-course
8 Awesome AI Agents
🔗 https://github.com/e2b-dev/awesome-ai-agents
9 GenAI Agents
🔗 https://github.com/NirDiamant/GenAI_Agents
10 Agents Towards Production
🔗 https://github.com/NirDiamant/agents-towards-production
Anything else you'd add? | 5 067 |
| 5 | 📚 7 Best Websites to Learn Computer Science Subjects for Free
More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true | 2 175 |
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| 7 |  You can literally enter a sentence and see how the Transformer processes it.
This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process:
Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token.
You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation.
If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this.
https://poloclub.github.io/transformer-explainer/
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| 13 | Tracking experiments and versioning ML models with MLflow. 🤖
Machine learning development requires saving hyperparameters, metrics, and artifacts for each run to compare results. The MLflow platform logs key metrics and registers trained models in a central registry. We will install MLflow, run a script to log parameters, and register the model.
Let's install the mlflow and scikit-learn libraries to conduct and log a test experiment. 📦
pip install mlflow scikit-learn
The dependencies for managing ML experiments have been successfully installed. ✅
Now, let's create a Python script called train.py that will train a simple model, log metrics, and save it to MLflow. 🐍
import mlflow
from sklearn.ensemble import RandomForestClassifier
mlflow.set_experiment("demo_experiment")
with mlflow.start_run():
params = {"n_estimators": 100, "max_depth": 5}
mlflow.log_params(params)
model = RandomForestClassifier(**params)
mlflow.log_metric("accuracy", 0.95)
mlflow.sklearn.log_model(model, "rf_model")
The training script and metric logging are set up and ready to be executed. 🚀
Let's run the training script to capture the results and parameters in the local MLflow storage.
python3 train.py
The experiment has been successfully completed, and the parameters and model artifacts have been saved. 📊
# verification (check for registered runs in MLflow)
mlflow runs list --experiment-name demo_experiment
Expected output:
demo_experiment ... FINISHED
# cleanup (remove the generated directory with artifacts and the script)
rm -rf mlruns train.py
Using MLflow helps avoid chaos when tuning hyperparameters and ensures reproducibility of results. Deploy an MLflow server on a separate host for the entire team to collaborate on the project. 🌐
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