Machine Learning
Real Machine Learning β simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatishπ Telegram kanali Machine Learning analitikasi
Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 41 884 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 201-o'rinni va Suriya mintaqasida 211-o'rinni egallagan.
π Auditoriya koβrsatkichlari va dinamika
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βReal Machine Learning β simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Yuqori yangilanish chastotasi (oxirgi maβlumot 26 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taβsir nuqtasiga aylantirishini koβrsatadi.
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| 2 | π 637 interactive modules on ML and AI
On VizLearn, you can visually explore:
π«‘ Gradient descent;
π«‘ SVM and PCA;
π«‘ Bayes' theorem;
π«‘ BPE tokenization;
π«‘ Query, Key, and Value;
π«‘ KV cache;
π«‘ Quantization;
π«‘ Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
β Link to the tutorial
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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
π https://github.com/Shubhamsaboo/awesome-llm-apps
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π https://github.com/microsoft/generative-ai-for-beginners
3 LLM Course
π https://github.com/mlabonne/llm-course
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π https://github.com/dair-ai/Prompt-Engineering-Guide
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π https://github.com/microsoft/ai-agents-for-beginners
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10 Agents Towards Production
π https://github.com/NirDiamant/agents-towards-production
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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 β Embeddings β Q/K/V β Attention Mechanism β MLP β Logits β 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. π€
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. π¦
pip install mlflow scikit-learn
The dependencies for managing ML experiments have been successfully installed. β
Now, let's create a Python script called train.py that will train a simple model, log metrics, and save it to MLflow. π
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. π
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. π
# 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. π
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