Artificial Intelligence & ChatGPT Prompts

前往频道在 Telegram

🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

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网络:Data Analytics 印度9 161 技术与应用3 180...

📈 Telegram 频道 Artificial Intelligence & ChatGPT Prompts 的分析概览

频道 Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) 英语语言赛道中的是活跃参与者。目前社区聚集了 42 072 名订阅者，在 技术与应用 类别中位列第 3 180，并在印度地区排名第 9 161 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 42 072 名订阅者。

根据 13 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -57，过去 24 小时变化为 -4，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 1.54%。内容发布后 24 小时内通常能获得 0.69% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 646 次浏览，首日通常累积 292 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 1。
主题关注点： 内容集中在 learning, algorithm, detection, llm, pattern 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data”

凭借高频更新（最新数据采集于 14 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

42 072

订阅者

-424 小时

-97 天

-5730 天

646

帖子浏览量

~ 29224 小时

~ 37948 小时

1.54%

参与率

~ 2

每日帖子数

Ads index

beta

数据加载中...

吸引订阅者

七月 '26

+80

在0个频道中

六月 '26

+210

在0个频道中

Get PRO

五月 '26

+362

在1个频道中

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四月 '26

+184

在0个频道中

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三月 '26

+119

在0个频道中

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二月 '26

+422

在1个频道中

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一月 '26

+597

在1个频道中

Get PRO

十二月 '25

+579

在0个频道中

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十一月 '25

+679

在1个频道中

Get PRO

十月 '25

+688

在2个频道中

Get PRO

九月 '25

+427

在0个频道中

Get PRO

八月 '25

+536

在3个频道中

Get PRO

七月 '25

+620

在2个频道中

Get PRO

六月 '25

+1 106

在4个频道中

Get PRO

五月 '25

+2 255

在4个频道中

Get PRO

四月 '25

+3 437

在2个频道中

Get PRO

三月 '25

+975

在4个频道中

Get PRO

二月 '25

+631

在7个频道中

Get PRO

一月 '25

+522

在3个频道中

Get PRO

十二月 '24

+274

在0个频道中

Get PRO

十一月 '24

+692

在3个频道中

Get PRO

十月 '24

+989

在0个频道中

Get PRO

九月 '24

+1 757

在1个频道中

Get PRO

八月 '24

+2 074

在1个频道中

Get PRO

七月 '24

+2 844

在3个频道中

Get PRO

六月 '24

+2 467

在2个频道中

Get PRO

五月 '24

+2 070

在5个频道中

Get PRO

四月 '24

+1 948

在3个频道中

Get PRO

三月 '24

+2 755

在2个频道中

Get PRO

二月 '24

+3 503

在1个频道中

Get PRO

一月 '24

+3 236

在3个频道中

Get PRO

十二月 '23

+2 129

在2个频道中

Get PRO

十一月 '23

+682

在6个频道中

Get PRO

十月 '23

+697

在0个频道中

Get PRO

九月 '23

+636

在0个频道中

Get PRO

八月 '23

+3 531

在0个频道中

日期	订阅者增长	提及	频道
14 七月	+6
13 七月	+6
12 七月	+3
11 七月	0
10 七月	+4
09 七月	+17
08 七月	+3
07 七月	0
06 七月	+3
05 七月	+5
04 七月	+14
03 七月	+1
02 七月	+11
01 七月	+7

频道帖子

✅ AI News of the Day: 13 July 2026 1️⃣ Google expands Gemini AI across Workspace Google has introduced new Gemini-powered features for Gmail, Docs, Sheets, and Meet, helping users automate writing, summarize documents, analyze data, and improve meeting productivity. 2️⃣ NVIDIA continues its AI infrastructure growth NVIDIA is strengthening its leadership in AI computing by expanding partnerships with cloud providers and enterprises to meet the growing demand for AI training and inference. 3️⃣ AI coding assistants gain wider enterprise adoption More organizations are integrating AI coding assistants into their development workflows, enabling developers to generate code, debug applications, and speed up software delivery. 4️⃣ AI-powered search is reshaping the web Technology companies continue to enhance AI-powered search experiences by providing conversational answers, summaries, and deeper reasoning capabilities instead of traditional search results. 5️⃣ Demand for AI talent keeps rising globally Companies across industries are actively hiring professionals with skills in Generative AI, machine learning, prompt engineering, AI agents, and automation as AI adoption continues to grow. 💬 Tap ❤️ for more!

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3	🚀 AI Fundamentals for Beginners: Part 2 Before building AI Agents or RAG applications, you should understand how Large Language Models LLMs actually work. Let's learn the core concepts. 🎯 1. What is a Large Language Model LLM? ✅ A Large Language Model LLM is an AI model trained on massive amounts of text to understand and generate human-like language. Popular examples: • GPT • Claude • Gemini • Llama • Mistral • DeepSeek LLMs can: ✅ Answer questions ✅ Write code ✅ Summarize documents ✅ Translate languages ✅ Generate content 🎯 2. What is a Prompt? ✅ A prompt is the instruction or input you provide to an AI model. Example: "What are the benefits of Python for Data Analysis?" The quality of your prompt often determines the quality of the response. 🎯 3. What are Tokens? ✅ AI models don't read entire sentences at once. Instead, they break text into smaller units called tokens. Example: Sentence: "I love Artificial Intelligence." May be split into multiple tokens before processing. More tokens = More processing cost and longer response time. 🎯 4. What is a Context Window? ✅ A context window is the maximum amount of information an LLM can process in a single conversation. It includes: • Your prompt • Previous conversation • Uploaded documents • AI responses A larger context window allows the model to remember and reason over more information. 🎯 5. What are Parameters? ✅ Parameters are the values learned by an AI model during training. In general: More parameters → Greater learning capacity However, performance also depends on training data, architecture, and optimization—not just parameter count. 🎯 6. What are Embeddings? ✅ Embeddings convert text into numerical vectors that capture its meaning. This allows AI systems to compare semantic similarity instead of just matching keywords. Embeddings are used for: ✅ Semantic Search ✅ Recommendation Systems ✅ Document Retrieval ✅ Similarity Search 🎯 7. What is a Vector Database? ✅ A vector database stores embeddings and enables fast similarity search. Popular Vector Databases: • Chroma • Pinecone • Weaviate • FAISS Without a vector database, efficient semantic search across large collections of documents becomes difficult. 🎯 8. How Does an AI Application Work? Basic Flow: User Question ⬇️ Prompt ⬇️ LLM ⬇️ Generated Response When external knowledge is needed: User Question ⬇️ Embedding ⬇️ Vector Database ⬇️ Relevant Information ⬇️ LLM ⬇️ Accurate Response 🎯 9. Why Are These Concepts Important? Understanding these concepts helps you build: ✅ AI Chatbots ✅ AI Assistants ✅ Enterprise Search ✅ Document Q&A Systems ✅ AI Agents 💡 Key Takeaway LLMs generate responses, embeddings help AI understand meaning, and vector databases make it possible to retrieve the right information quickly. Together, they form the foundation of modern AI applications. ❤️ Double Tap ❤️ For More	374
4	🚀 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 - 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 If you’re serious about starting your career in tech, this is one opportunity you shouldn’t miss 🚀 ✅ 2000+ Students Already Placed 🤝 500+ Hiring Partners 💼 Salary: ₹7.4 LPA 🚀 Highest Package: ₹41 LPA 💻 Get trained in in-demand tech skills 👨‍🏫 Learn from industry experts 📈 Get dedicated placement support 💸 Pay only after you land a job 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:- https://pdlink.in/42WOE5H Hurry! Limited seats are available.🏃‍♂️	353
5	🎓 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲 Boost your resume with Industry-recognized certifications without spending a single rupee 🌟 📚 Available from: ✅ Google ✅ Microsoft ✅ Cisco ✅ IBM ✅ HP ✅ Qualcomm ✅ TCS ✅ Infosys 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/3SNiXKz 🚀 Don't miss these FREE certification opportunities in 2026!	381
6	Most AI engineers never fully understood the maths behind what they build! 🤯🧮 This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨ Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗 What it covers: - Vectors, linear algebra, calculus, and optimization 📐📉 - Classical machine learning and deep learning 🤖 - Transformer architectures and LLMs 🦄 - Efficient architectures, quantization, and distillation ⚡️ - CUDA, GPU programming, and SIMD 🚀 - AI inference and deployment 🌐 Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗 🌐 Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium	409
7	𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗲𝘀𝗲 𝗛𝗶𝗴𝗵-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯𝘀 🔥 This guide highlights 3 powerful skills that are opening doors to high-paying roles across tech and business .🎓 Perfect For 👨‍🎓 Students 💼 Freshers 📈 Job seekers trying to improve employability 🚀 Anyone who wants to build a future-proof career with better salary potential 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4vXeGmm 🚀 Start learning today. Build in-demand skills. Position yourself for better opportunities and bigger career growth.	454
8	✅ Today's AI News 1️⃣ OpenAI is pushing ahead with GPT-5.6 Recent coverage says OpenAI is preparing a broader GPT-5.6 rollout, with the model family getting new tiers and wider use across products. 2️⃣ Meta is racing on AI image and coding tools Meta has been expanding its AI push with new image and video models, while also moving further into AI coding competition. 3️⃣ Governments are watching AI more closely Regulators are focusing on model safety, overseas access, copyright, and how AI content is used in news and business. 4️⃣ AI safety is back in the spotlight New reports continue to question whether major AI labs are moving fast enough on safety testing and governance. 5️⃣ India remains an important AI market Indian coverage shows strong interest in AI hiring, policy, enterprise deployment, and the role of local operations from major AI firms. 💬 Tap ❤️ for more!	513
9	𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀🎓 Offers a wide range of free learning resources through Microsoft Learn, helping students, freshers, and professionals build job-ready skills at their own pace. ✅ 100% FREE self-paced learning modules ✅ Official learning platform from Microsoft 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4paqRJS Explore Microsoft’s free resources. Build in-demand skills and make your profile stronger.	485
10	Questions they may evaluate: Did the tool return valid data? Is another tool required? Is the answer complete? Should I retry? Reflection improves reliability. 10. Final Response After completing all required steps, the agent generates the final answer for the user. 🔄 Complete Workflow Example User Goal: Find the latest AI news and summarize it. Step 1: Understand the request. Step 2: Plan → Search news → Read articles → Summarize → Highlight key trends Step 3: Use web search tool. Step 4: Collect results. Step 5: Summarize findings. Step 6: Return final response. 🧠 Why Planning is Important Without planning: Question → Random answer With planning: Question → Break into tasks → Execute tasks → Verify results → Final answer Planning makes agents more accurate and capable. 🛠️ Common Tools Used by AI Agents Web Search: Retrieve current information Python: Data analysis and automation SQL: Query databases Browser: Navigate websites Email: Send messages Calendar: Schedule meetings File System: Read and write files APIs: Connect with external services 📚 Example: AI Data Analyst Agent Goal: Analyze a sales CSV. Workflow: Upload CSV → Read File → Clean Data → Analyze Trends → Generate Charts → Create Business Insights → Export Report 🤖 Example: AI Coding Agent Workflow: User Request → Understand Problem → Generate Code → Run Tests → Fix Errors → Return Working Code 🌍 Example: AI Travel Agent Workflow: Travel Request → Search Flights → Search Hotels → Compare Prices → Create Itinerary → Present Best Options 🚀 Key Takeaways An AI agent is much more than a chatbot—it can plan, reason, use tools, and adapt. The core architecture: User Input → Prompt Processing → LLM → Memory → Planning → Tool Selection → Action Execution → Observation → Reflection → Final Response. Planning, memory, and tool usage are what make AI agents capable of solving real-world, multi-step problems. Double Tap ❤️ For More	425
11	🚀 AI Agents Architecture Explained After understanding the basics of AI agents, the next step is learning how an AI agent works internally. Every AI agent, whether it's a customer support bot, coding assistant, or research assistant, follows a similar architecture. 🏗️ What is AI Agent Architecture? AI Agent Architecture is the blueprint that defines how an agent receives a task, thinks, plans, uses tools, remembers information, and delivers results. Think of it as the internal workflow that allows an AI agent to solve problems autonomously. 🔄 High-Level AI Agent Architecture User │ ▼ User Request/Goal │ ▼ Prompt Processing │ ▼ Reasoning (LLM) │ ┌───────┴────────┐ ▼ ▼ Memory Tool Selection │ │ └───────┬────────┘ ▼ Task Planning ▼ Action Execution ▼ Observe Results ▼ Reflection & Retry ▼ Final Response 🧩 Components of an AI Agent 1. User Input The process starts when a user provides a goal. Examples: "Analyze this sales data." "Book a hotel in Mumbai." "Write a Python script." The agent first understands what needs to be achieved, not just what was typed. 2. Prompt Processing The system combines: User prompt, System instructions, Conversation history, Available tools, Memory This creates the complete context for the LLM. 3. LLM (Reasoning Engine) The LLM acts as the brain. Responsibilities: Understand the request, Decide what to do, Select tools if required, Generate a plan, Interpret results Without an LLM, an AI agent cannot reason effectively. 4. Memory Memory allows the agent to retain useful information. Short-Term Memory: Current conversation, Intermediate steps Long-Term Memory: User preferences, Past interactions, Frequently used information Example: If you always prefer Python over Java, the agent can remember that for future tasks. 5. Planning Module Complex tasks are broken into smaller steps. Example Goal: "Create a monthly sales report." Plan: 1. Load data 2. Clean missing values 3. Calculate KPIs 4. Create charts 5. Generate summary 6. Export PDF Planning improves efficiency and reduces errors. 6. Tool Selection The agent decides whether external tools are needed. Possible tools: Web search, SQL database, Python interpreter, Calculator, Email API, Calendar, Browser automation Example: For "What's today's weather?", the agent chooses a weather API instead of guessing. 7. Action Execution The selected tool performs the required action. Examples: Execute SQL query, Run Python code, Search the web, Read a PDF, Send an email 8. Observation After using a tool, the agent receives the result. Example: Tool: Weather API Observation: Temperature = 30°C, Humidity = 72% The observation becomes new input for the next reasoning step. 9. Reflection Advanced agents verify their work.	319
12	𝗔𝗜 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 😍 💫 Join this live masterclass and gain practical insights into AI-powered Product Management, in-demand skills 💫Roadmap to building a successful Product Management career Eligibility :- Recent Graduates & Working Professionals 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇 :- https://pdlink.in/44VeqIA ( Limited Slots ..Hurry Up‍ ) Date & Time :- 11th July 2026 , 8:00 PM (IST)	391
13	没有文字...	486
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15	10 Retro Nano Banana 3D Figurine Prompts 🔹 Prompt: Turn the image into a pop art-style 3D figurine, featuring bold colors, halftone dots, and comic-book speech bubbles around the character. 🔹 Prompt: Make a collectible figure inspired by 1950s diners, with a checkered floor base, red booth, and soda fountain props. 🔹 Prompt: Stylize the photo as a 1970s hippie figurine, with peace sign necklace, colorful headband, and a tie-dye shirt against a psychedelic abstract background. 🔹 Prompt: Reimagine the subject as a retro video game character in 16-bit pixel art style, with the character placed on a simulated arcade platform. 🔹 Prompt: Generate a vintage sci-fi astronaut figurine, featuring metallic suit details, ray-gun prop, and a rocket backdrop reminiscent of classic sci-fi movies. 🔹 Prompt: Produce a golden-age Bollywood collectible, complete with sari, retro hairstyle, and filmstrip base; add a vintage film poster in the background. 🔹 Prompt: Create a figurine styled after 1960s mod fashion—buttoned mini-dress, go-go boots, and psychedelic swirl base. 🔹 Prompt: Make a collectible in a retro comic superhero look, with bold primary colors, classic mask, and golden-age comic effects in the foreground. Double Tap ❤️ for more	612
16	📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀 ✅ 100% FREE learning opportunities ✅ Great for students, freshers, and beginners ✅ Help you build a stronger resume with recognized names like Cisco, Google, and Microsoft ✅ Useful for analytics internships, off-campus drives, and fresher hiring 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4eRA6eF 🚀 Start learning today. Build your analytics foundation. Earn free certifications. Move one step closer to your Data Analyst career.	584
17	Data Science Roadmap \| \|-- Core Foundations \| \|-- Mathematics \| \| \|-- Linear Algebra \| \| \|-- Calculus Basics \| \| \|-- Probability \| \| \|-- Statistics \| \| \| \|-- Programming \| \| \|-- Python \| \| \| \|-- NumPy \| \| \| \|-- Pandas \| \| \| \|-- Matplotlib \| \| \| \|-- Seaborn \| \| \|-- R \| \| \|-- SQL \| \|-- Data Handling \| \|-- Data Collection \| \| \|-- APIs \| \| \|-- Web Scraping \| \| \|-- Database Queries \| \| \| \|-- Data Cleaning \| \| \|-- Missing Values \| \| \|-- Outliers \| \| \|-- Feature Scaling \| \| \|-- Encoding \| \|-- Exploratory Data Analysis \| \|-- Summary Statistics \| \|-- Univariate Analysis \| \|-- Bivariate Analysis \| \|-- Visualizations \| \|-- Correlation Checks \| \|-- Machine Learning \| \|-- Supervised Learning \| \| \|-- Regression \| \| \|-- Classification \| \| \| \|-- Unsupervised Learning \| \| \|-- Clustering \| \| \|-- PCA \| \| \| \|-- Model Selection \| \| \|-- Train Test Split \| \| \|-- Cross Validation \| \| \|-- Hyperparameter Tuning \| \|-- Advanced Machine Learning \| \|-- Ensemble Methods \| \| \|-- Random Forest \| \| \|-- XGBoost \| \| \|-- LightGBM \| \| \| \|-- Time Series \| \| \|-- ARIMA \| \| \|-- LSTM \| \| \| \|-- NLP \| \| \|-- Text Preprocessing \| \| \|-- TF IDF \| \| \|-- Word Embeddings \| \| \| \|-- Deep Learning \| \| \|-- Neural Networks \| \| \|-- CNN \| \| \|-- RNN \| \| \|-- Transformers \| \|-- Big Data \| \|-- PySpark \| \|-- Hadoop \| \|-- Distributed Processing \| \|-- Model Deployment \| \|-- Flask \| \|-- FastAPI \| \|-- Streamlit \| \|-- Docker \| \|-- Cloud Deployment \| \|-- MLOps \| \|-- Experiment Tracking \| \|-- Model Monitoring \| \|-- CI CD \| \|-- Domain Knowledge \| \|-- Finance \| \|-- Healthcare \| \|-- Retail \| \|-- Marketing \| \|-- Ethics \| \|-- Bias \| \|-- Interpretability \| \|-- Fairness Free Resources to learn Data Science 👇👇 Python • https://t.me/pythonproz • https://www.learnpython.org/ • https://pythonprogramming.net • https://pandas.pydata.org/docs/ Statistics • https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O • https://www.khanacademy.org/math/statistics-probability • https://statquest.org Machine Learning • https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O • https://t.me/datasciencefree • https://scikit-learn.org/stable/tutorial • https://www.freecodecamp.org/learn/machine-learning-with-python • https://course.fast.ai Deep Learning • https://www.deeplearning.ai • https://playground.tensorflow.org Data Visualization • https://matplotlib.org/stable/tutorials • https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34 • https://seaborn.pydata.org/tutorial.html SQL • https://mode.com/sql-tutorial/introduction-to-sql • https://t.me/mysqldata Big Data • https://spark.apache.org/docs/latest • https://hadoop.apache.org Deployment • https://docs.streamlit.io • https://fastapi.tiangolo.com Like for more ❤️ ENJOY LEARNING 👍👍	572
18	🎓 𝗧𝗼𝗽 𝟱 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀𝗲𝘁 🚀 These 5 FREE courses that can help you stand out in interviews and job applications! 💼✨ 📊 Microsoft Excel 📈 Power BI 💫 Python for Data Science ⏰Time Management 💰 Basic Financial Accounting 🎯 Invest a few hours today to unlock better career opportunities tomorrow! 🔗 𝗟𝗲𝗮𝗿𝗻 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:- https://pdlink.in/4dPjz92 📌 Save this post and share it with friends looking to upskill in 2026.	527
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20	𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 \| 𝟱 𝗠𝘂𝘀𝘁-𝗪𝗮𝘁𝗰𝗵 𝗙𝗥𝗘𝗘 𝗩𝗶𝗱𝗲𝗼𝘀 🚀 The good news is — you don’t need expensive courses to understand the basics of AI, Machine Learning, Neural Networks, Prompting, and real-world AI tools. This guide features 5 must-watch FREE AI videos that can help you build a strong foundation in AI concepts 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4gn4LS5 🚀 Start watching today. Learn AI step by step. Build future-ready skills for free.	654

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