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AI and Machine Learning

AI and Machine Learning

رفتن به کانال در Telegram

Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

نمایش بیشتر

📈 تحلیل کانال تلگرام AI and Machine Learning

کانال AI and Machine Learning (@machine_learning_courses) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 94 048 مشترک است و جایگاه 1 561 را در دسته آموزش و رتبه 3 020 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 94 048 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 24 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 986 و در ۲۴ ساعت گذشته برابر 67 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 6.50% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.56% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 6 109 بازدید دریافت می‌کند. در اولین روز معمولاً 1 470 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 8 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, llm, linkedin, linux, udemy تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 25 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

94 048
مشترکین
+6724 ساعت
+1517 روز
+98630 روز
آرشیو پست ها
🔗 Roadmap to master Machine Learning
+4
🔗 Roadmap to master Machine Learning

🔗 Roadmap to master Machine Learning
+4
🔗 Roadmap to master Machine Learning

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🔗 The Only roadmap you need to become an ML Engineer 🥳 Phase 1: Foundations (1-2 Months) 🔹 Math & Stats Basics – Linear Algebra, Probability, Statistics 🔹 Python Programming – NumPy, Pandas, Matplotlib, Scikit-Learn 🔹 Data Handling – Cleaning, Feature Engineering, Exploratory Data Analysis Phase 2: Core Machine Learning (2-3 Months) 🔹 Supervised & Unsupervised Learning – Regression, Classification, Clustering 🔹 Model Evaluation – Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC) 🔹 Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization 🔹 Basic ML Projects – Predict house prices, customer segmentation Phase 3: Deep Learning & Advanced ML (2-3 Months) 🔹 Neural Networks – TensorFlow & PyTorch Basics 🔹 CNNs & Image Processing – Object Detection, Image Classification 🔹 NLP & Transformers – Sentiment Analysis, BERT, LLMs (GPT, Gemini) 🔹 Reinforcement Learning Basics – Q-learning, Policy Gradient Phase 4: ML System Design & MLOps (2-3 Months) 🔹 ML in Production – Model Deployment (Flask, FastAPI, Docker) 🔹 MLOps – CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow) 🔹 Cloud & Big Data – AWS/GCP/Azure, Spark, Kafka 🔹 End-to-End ML Projects – Fraud detection, Recommendation systems Phase 5: Specialization & Job Readiness (Ongoing) 🔹 Specialize – Computer Vision, NLP, Generative AI, Edge AI 🔹 Interview Prep – Leetcode for ML, System Design, ML Case Studies 🔹 Portfolio Building – GitHub, Kaggle Competitions, Writing Blogs 🔹 Networking – Contribute to open-source, Attend ML meetups, LinkedIn presence The data field is vast, offering endless opportunities so start preparing now.

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🔅 08 - FineTuning LLMs

🔅 07 - LLM Context Window

🔅 06 - LLM Parameters

🔅 05 - Inside an LLM Under the Hood

🔅 04 - How LLMs generate text

🔅 03 - Who invented LLMs

🔅 02 - Ingredients of an LLM

🔅 01 - Introduction to LLMs

LLMs Intuition 01 - Introduction to LLMs 02 - Ingredients of an LLM 03 - Who invented LLMs 04 - How LLMs generate text 05 - Inside an LLM Under the Hood 06 - LLM Parameters 07 - LLM Context Window 08 - FineTuning LLMs

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📦 Exercise Files