Artificial Intelligence

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🔰 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:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert Hindiston7 029 Taʼlim3 254...

📈 Telegram kanali Artificial Intelligence analitikasi

Artificial Intelligence (@machinelearning_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 53 207 obunachidan iborat bo'lib, Taʼlim toifasida 3 254-o'rinni va Hindiston mintaqasida 7 029-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 53 207 obunachiga ega bo‘ldi.

10 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 1 050 ga, so‘nggi 24 soatda esa 35 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 5.80% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.68% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 3 086 marta ko‘riladi; birinchi sutkada odatda 892 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 9 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 11 Iyun, 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.

53 207

Obunachilar

+3524 soatlar

+1927 kunlar

+1 05030 kunlar

3 086

Post ko'rishlar

~ 89224 soatlar

~ 1 13048 soatlar

5.80%

Muloqot nisbati

Ma'lumot yo'q

Kuniga postlar

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Postlar arxiv

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2. Mock Interview Practice: Create a mock interview scenario for the [JOB TITLE] role at [SPECIFIC COMPANY]. Include 5 common and challenging questions I might face, and provide guidance on how to answer each effectively.

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1. Developing STAR Method Responses: Help me craft a STAR (Situation, Task, Action, Result) response to the interview question: [INSERT QUESTION] for the [JOB TITLE] role. Ensure the response is clear, concise, and demonstrates my impact in previous roles.

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Here are 10 ChatGPT-4o Prompts you need to know to Dominate and Excel at any job interview:

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#meme

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Data Science Essentials in Python.pdf5.01 MB

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8. Set up the user interface and trigger the main function. • Provides an input field for the user's question • Triggers the main function when the user clicks "Get Answer"

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7. Define the main function to run all LLMs and aggregate results. • Runs all reference models asynchronously • Displays individual responses in expandable sections • Aggregates responses using the aggregator model • Streams the aggregated response.

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6. Implement the LLM call function. • Asynchronously calls the LLM with the user's prompt • Returns the model name and its response

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5. Define the models and aggregator system prompt. • Specifies the LLMs to be used for generating responses • Defines the aggregator model and its system prompt

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4. Initialize Together AI clients. • Sets up Together API key as an environment variable • Initializes both synchronous and asynchronous Together clients

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3. Set up the Streamlit app and API key input. • Creates a title for the app • Adds a secure input field for the Together API key

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2. Import necessary libraries • Streamlit for the web interface • asyncio for asynchronous operations • Together AI for LLM interactions

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1. Install the necessary Python Libraries Run the following commands from your terminal to install the required libraries:

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Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code (step-by-step instructions): ⬇️

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You can use ChatGPT to make money online. Here are 10 prompts by ChatGPT 1. Develop Email Newsletters: Make interesting email newsletters to keep audience updated and engaged. Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?" 2. Create Online Course Material: Make detailed and educational online course content. Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?" Read more......

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Machine_Learning_in_Finance_From_Theory_to_Practice_Matthew_F_Dixon.pdf8.75 MB

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Free ML crash course by Google 👇👇 https://developers.google.com/machine-learning/crash-course/

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Matrix Theory and Linear Algebra Peter Selinger, 2018