متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Artificial Intelligence
الذهاب إلى القناة على Telegram
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Artificial Intelligence
تُعد قناة Artificial Intelligence (@machinelearning_deeplearning) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 53 161 مشتركاً، محتلاً المرتبة 3 256 في فئة التعليم والمرتبة 7 041 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 53 161 مشتركاً.
بحسب آخر البيانات بتاريخ 09 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 1 045، وفي آخر 24 ساعة بمقدار 38، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 5.69%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.68% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 3 022 مشاهدة. وخلال اليوم الأول يجمع عادةً 892 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 9.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, classification, layer, pattern, chatbot.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 10 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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المشتركون
+3824 ساعات
+1977 أيام
+1 04530 أيام
أرشيف المشاركات
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𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝘄𝗶𝘁𝗵 𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!😍
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> How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
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Here are five of the most commonly used SQL queries in data science:
1. SELECT and FROM Clauses
- Basic data retrieval:
SELECT column1, column2 FROM table_name;
2. WHERE Clause
- Filtering data: SELECT * FROM table_name WHERE condition;
3. GROUP BY and Aggregate Functions
- Summarizing data: SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1;
4. JOIN Operations
- Combining data from multiple tables:
SELECT a.column1, b.column2
FROM table1 a
JOIN table2 b ON a.common_column = b.common_column;
SELECT column1
FROM table_name
WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);53 173
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 & 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 😍
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If you want to Excel in AI and become an expert, master these essential concepts:
Core AI Concepts:
• Machine Learning (ML) – Supervised, Unsupervised, and Reinforcement Learning
• Deep Learning (DL) – Neural Networks, CNNs, RNNs, Transformers
• Natural Language Processing (NLP) – Text processing, LLMs (GPT, BERT)
• Computer Vision (CV) – Image classification, Object detection
• AI Ethics & Bias – Responsible AI development
Essential AI Tools & Frameworks:
• Python Libraries – TensorFlow, PyTorch, Scikit-Learn, Keras
• Data Processing – Pandas, NumPy, OpenCV, NLTK, SpaCy
• Pretrained Models – OpenAI GPT, Stable Diffusion, DALL·E, CLIP
• MLOps & Deployment – Docker, FastAPI, Hugging Face, Flask, Gradio
Mathematical Foundations:
• Linear Algebra – Vectors, Matrices, Tensors
• Probability & Statistics – Bayes’ Theorem, Hypothesis Testing
• Optimization – Gradient Descent, Backpropagation
AI in Real-World Applications:
• Chatbots & Virtual Assistants – Build AI-powered bots
• Recommendation Systems – Personalized content suggestions
• Autonomous Systems – Self-driving cars, Robotics
• AI in Healthcare – Disease prediction, Medical imaging
Future Trends in AI:
• AGI (Artificial General Intelligence) – Next-level AI development
• AI in Business & Automation – AI-powered decision-making
• Low-Code/No-Code AI – Democratizing AI for everyone
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Python is more popular than other programming languages because:
1. Easy to Learn and Use
2. Versatility (Used everywhere in various tech field)
3. Huge Community & Support
4. Cross-Platform Compatibility (works on windows, macos, linux and even on mobile operating system)
5. Strong Industry Adoption
6. Rich Ecosystem & Libraries (Examples: Django (web), TensorFlow (AI), PyGame (game development), and BeautifulSoup (web scraping).)
7. Support for AI & Machine Learning
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Basic skills needed for ai engineer
1. Programming Skills (Essential)
Learn Python (most widely used in AI).
Basics of libraries like NumPy, Pandas (for data handling).
Understanding of loops, functions, OOPs concepts.
2. Mathematics & Statistics (Basic Level)
Linear Algebra (Vectors, Matrices, Dot Product).
Probability & Statistics (Mean, Variance, Standard Deviation).
Basic Calculus (Derivatives, Integrals – useful for ML models)
3. Machine Learning Fundamentals
Understand what Supervised & Unsupervised Learning are.
Learn about Regression, Classification, and Clustering.
Introduction to Neural Networks and Deep Learning.
4. Data Handling & Processing
How to collect, clean, and process data for AI models.
Using Pandas & NumPy to manipulate datasets.
5. AI Libraries & Frameworks
Learn Scikit-learn for ML models.
Introduction to TensorFlow or PyTorch for Deep Learning.
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𝟱 𝗕𝗲𝘀𝘁 𝗜𝗕𝗠 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
1)Python for Data Science
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3)Applied Data Science with Python
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5)Data Analysis with Python
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Master AI (Artificial Intelligence) in 10 days 👇👇
#AI
Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.
Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.
Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.
Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.
Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.
Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.
Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.
Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.
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Advanced AI and Data Science Interview Questions
1. Explain the concept of Generative Adversarial Networks (GANs). How do they work, and what are some of their applications?
2. What is the Curse of Dimensionality? How does it affect machine learning models, and what techniques can be used to mitigate its impact?
3. Describe the process of hyperparameter tuning in deep learning. What are some strategies you can use to optimize hyperparameters?
4. How does a Transformer architecture differ from traditional RNNs and LSTMs? Why has it become so popular in natural language processing (NLP)?
5. What is the difference between L1 and L2 regularization, and in what scenarios would you prefer one over the other?
6. Explain the concept of transfer learning. How can pre-trained models be used in a new but related task?
7. Discuss the importance of explainability in AI models. How do methods like LIME or SHAP contribute to model interpretability?
8. What are the differences between Reinforcement Learning (RL) and Supervised Learning? Can you provide an example where RL would be more appropriate?
9. How do you handle imbalanced datasets in a classification problem? Discuss techniques like SMOTE, ADASYN, or cost-sensitive learning.
10. What is Bayesian Optimization, and how does it compare to grid search or random search for hyperparameter tuning?
11. Describe the steps involved in developing a recommendation system. What algorithms might you use, and how would you evaluate its performance?
12. Can you explain the concept of autoencoders? How are they used for tasks such as dimensionality reduction or anomaly detection?
13. What are adversarial examples in the context of machine learning models? How can they be used to fool models, and what can be done to defend against them?
14. Discuss the role of attention mechanisms in neural networks. How have they improved performance in tasks like machine translation?
15. What is a variational autoencoder (VAE)? How does it differ from a standard autoencoder, and what are its benefits in generating new data?
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