Artificial Intelligence
Kanalga Telegram’da o‘tish
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Artificial Intelligence analitikasi
Artificial Intelligence (@machinelearning_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 55 377 obunachidan iborat bo'lib, Taʼlim toifasida 3 050-o'rinni va Hindiston mintaqasida 6 211-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 55 377 obunachiga ega bo‘ldi.
30 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 683 ga, so‘nggi 24 soatda esa 41 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 5.87% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.33% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 250 marta ko‘riladi; birinchi sutkada odatda 736 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 25 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, classification, layer, pattern, chatbot kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 31 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
55 377
Obunachilar
+4124 soatlar
+1517 kunlar
+68330 kunlar
Postlar arxiv
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Russia is currently hosting the AI Journey international conference, during which the second season of the AI4PLANET scientific and educational video podcast was released.
The main topic of this season was the role of AI in the emergence of new professions and transformation of existing ones. The speakers of the podcast discussed in 10 episodes how AI is already helping experts and what are the prospects of using AI in the work of ecologists, climatologists, doctors, teachers, HR-specialists and security officers.
The experts sought answers to the burning questions:
▫️ How will AI strengthen the skills of the in-demand specialist of the future?
▫️ Do scientists and researchers already need to master Data Science skills now?
▫️ AI-developer for sustainable development - a new profession or a collective image of coordinated interdisciplinary work of a large team?
AI4PLANET is a technological “journey” through professions from different fields of sustainable development: from climate and ecology to psychology and professions of the future.
We invite you to visit the AI Journey international conference page and listen to the AI4PLANET video podcast.
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ARTIFICIAL INTELLIGENCE 🤖
🎥 Siraj Raval - YouTube channel with tutorials about AI.
🎥 Sentdex - YouTube channel with programming tutorials.
⏱ Two Minute Papers - Learn AI with 5-min videos.
✍️ Data Analytics - blog on Medium.
🎓 Google Machine Learning Course - A crash course on machine learning taught by Google engineers.
🌐 Google AI - Learn from ML experts at Google.
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AI/ML Roadmap👨🏻💻👾🤖 -
==== Step 1: Basics ====
📊 Learn Math (Linear Algebra, Probability).
🤔 Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
🔢 Clean & Visualize Data (Pandas, Matplotlib).
🏋️♂️ Learn Core Algorithms (Linear Regression, Decision Trees).
📦 Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
💡 Understand Neural Networks.
🖼️ Learn TensorFlow or PyTorch.
🤖 Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
🌳 Study Advanced Algorithms (Random Forest, XGBoost).
🗣️ Dive into NLP or Computer Vision.
🕹️ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
🎨 Create real-world projects.
🌍 Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
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💻Free chat gpt alternative
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Without any login Infact you can upload your pdf documents to ask answer best site for students who were looking for chat gpt 4 now enjoy it
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💸 SECOND AND LAST TRADING SESSION: THE RESULTS SPEAK FOR THEMSELVES! 💸
Here is the end of the second trading session, and as always, I am thrilled with the results! 💥📈
It wasn't just a trading session - it was a WIN! Each of you can repeat these results just by following the signals and not being afraid to act! 🚀
We were able to earn $5,900 starting from $200! Anyone could do it.
From tomorrow we start a new marathon :
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⌛Deadline : 10 days!
Everyone can participate absolutely free of charge! Go to the channel and start trading. Write in private messages to Lisa : “TRADING”. At the link below
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10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
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Hard Pill To Swallow: 💊
Robots aren’t stealing your future - they’re taking the boring jobs.
Meanwhile:
- Some YouTuber made six figures sharing what she loves.
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.
Here’s the thing:
Hard work still matters. But the rules of the game have changed.
The real money is in solving problems, spreading ideas, and building cool stuff.
Call it evolution. Call it disruption. Whatever.
Crying about the old world won't help you thrive in the new one.
Create something.✨
#ai
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AI Engineer
Deep Learning: Neural networks, CNNs, RNNs, transformers.
Programming: Python, TensorFlow, PyTorch, Keras.
NLP: NLTK, SpaCy, Hugging Face.
Computer Vision: OpenCV techniques.
Reinforcement Learning: RL algorithms and applications.
LLMs and Transformers: Advanced language models.
LangChain and RAG: Retrieval-augmented generation techniques.
Vector Databases: Managing embeddings and vectors.
AI Ethics: Ethical considerations and bias in AI.
R&D: Implementing AI research papers.
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What kind of problems neural nets can solve?
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
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FREE Stock Marketing Resources
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