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

Ko'proq ko'rsatish

Tarmoq:Machine Learning with Python Suriya236 Texnologiyalar & Aralashmalar3 447...

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 39 901 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 447-o'rinni va Suriya mintaqasida 236-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 39 901 obunachiga ega bo‘ldi.

05 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 482 ga, so‘nggi 24 soatda esa 19 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 3.06% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.66% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 1 222 marta ko‘riladi; birinchi sutkada odatda 662 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 06 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

39 901

Obunachilar

+1924 soatlar

+1307 kunlar

+48230 kunlar

1 222

Post ko'rishlar

~ 66224 soatlar

~ 84548 soatlar

3.06%

Muloqot nisbati

~ 3

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Sana	Obunachilarni jalb qilish	Esdaliklar	Kanallar
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4	A new collection of free courses has been added: 🔗 https://github.com/dair-ai/ML-Course-Notes Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚 Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠 What's inside: • Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others • A table with lectures, descriptions, videos, notes, and authors • Links to the original lectures and accompanying notes • WIP markers for incomplete materials • Instructions for contributors on adding and improving notes The idea was appreciated. 👍 Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️ #MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO	504
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10	Optimizing the model's performance through Prompt Tuning with the PEFT library. ✨ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge. 📦 First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT). pip install torch transformers peft ✅ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters. from peft import PromptTuningConfig, PromptTuningInit, get_peft_model from transformers import AutoModelForCausalLM peft_config = PromptTuningConfig( task_type="CAUSAL_LM", prompt_tuning_init=PromptTuningInit.TEXT, num_virtual_tokens=20, prompt_tuning_init_text="Classify the sentiment of this text:", tokenizer_name_or_path="gpt2" ) 🔄 The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent. base_model = AutoModelForCausalLM.from_pretrained("gpt2") peft_model = get_peft_model(base_model, peft_config) peft_model.print_trainable_parameters() 🚀 The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%). python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')" 📝 Expected output: PEFT Setup: OK pip uninstall peft -y 💡 Prompt Tuning — an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference. #PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO	743
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18	+1 FREE MIT books on AI and Machine Learning: 📚🤖 1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/ 2. Understanding Deep Learning udlbook.github.io/udlbook/ 3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ Vol 2: mlsysbook.ai/vol2/assets/do 4. Algorithms for ML algorithmsbook.com 5. Deep Learning deeplearningbook.org 6. Reinforcement Learning andrew.cmu.edu/course/10-703/ 7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog 8. Multi Agent Reinforcement Learning marl-book.com 9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog 10. Fairness and Machine Learning fairmlbook.org 11. Probabilistic Machine Learning ❯ Part 1 : probml.github.io/pml-book/book1 ❯ Part 2 : probml.github.io/pml-book/book2 #MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A	3 080
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20	Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. 📉 The model didn't become smarter. It just happened to see the correct answers in advance. In 4 minutes, you'll understand where data leaks hide. 🔍 Let's break it down below: 👇 1. Data Leakage 🕳️ Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process. Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data. 2. Model Evaluation ⚖️ The test set isn't just "additional data". It's a simulation of the future. Only train the model on the information that would have been available to you at the time of prediction. Evaluate it on examples that the model couldn't have influenced during training. 3. Direct Leakage 🚨 This is the most obvious type of leakage. Examples: - a field with information from the future; - an ID that encodes the target variable; - a variable that appears only after an event has occurred; - duplicate records in both the training and test sets. If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage. 4. Indirect Leakage 🕵️ This is the type of leakage that most often traps teams. You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set. The model didn't directly see the data from the test set. But your preprocessing pipeline already saw it. 5. Train/Test Split ✂️ Wrong: fit the scaler on all data → split the data → evaluate Right: split the data → fit the scaler only on the training set → apply it to both the training and test sets The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data. 6. Cross-Validation 🔄 Each fold is a mini-experiment with a training and test set. Therefore, preprocessing should be performed within each fold. If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data. 7. Pipelines 🛠️ A pipeline isn't just a way to make the code cleaner. It's also a defense against data leakage. Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search). 8. AI Engineering Version 🤖 Data leaks also occur in RAG systems and when evaluating LLMs. Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out". As a result, your benchmark turns into training data. 9. Leakage Checklist ✅ Before trusting the obtained metric, ask yourself: - Could this feature exist at the time of prediction? - Was any transformation (transform) step trained (fit) on the test data? - Did cross-validation include the entire pipeline? - Were we tuning parameters on the final evaluation dataset? If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model. #MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A	3 024

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