Artificial Intelligence & ChatGPT Prompts

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🔓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

Network:Data Analytics India9 545 Technologies & Applications3 229...

📈 Analytical overview of Telegram channel Artificial Intelligence & ChatGPT Prompts

Channel Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) in the English language segment is an active participant. Currently, the community unites 42 123 subscribers, ranking 3 229 in the Technologies & Applications category and 9 545 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 42 123 subscribers.

According to the latest data from 12 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 175 over the last 30 days and by 12 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.43%. Within the first 24 hours after publication, content typically collects 0.73% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 024 views. Within the first day, a publication typically gains 306 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
Thematic interests: Content is focused on key topics such as learning, algorithm, detection, llm, pattern.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“🔓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”

Thanks to the high frequency of updates (latest data received on 13 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

42 123

Subscribers

+1224 hours

+227 days

+17530 days

1 024

Post views

~ 30624 hours

~ 43948 hours

2.43%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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Python PIP Cheatsheet 👆

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𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍 Breaking into Data Analytics isn’t just about knowing the tools — it’s about answering the right questions with confidence🧑‍💻✨️ Whether you’re aiming for your first role or looking to level up your career, these real interview questions will test your skills📊📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3JumloI Don’t just learn — prepare smart✅️

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Data Analyst Scenario based Question and Answers 👇👇 1. Scenario: Creating a Dynamic Sales Growth Report in Power BI Approach: Load Data: Import sales data and calendar tables. Data Model: Establish a relationship between the sales and calendar tables. Create Measures: Current Sales: Current Sales = SUM(Sales[Amount]). Previous Year Sales: Previous Year Sales = CALCULATE(SUM(Sales[Amount]), DATEADD(Calendar[Date], -1, YEAR)). Sales Growth: Sales Growth = [Current Sales] - [Previous Year Sales]. Visualization: Use Line Chart for trends. Use Card Visual for displaying numeric growth values. Slicers and Filters: Add slicers for selecting specific time periods. 2. Scenario: Identifying Top 5 Customers by Revenue in SQL Approach: Understand the Schema: Know the relevant tables and columns, e.g., Orders table with CustomerID and Revenue. SQL Query: SELECT TOP 5 CustomerID, SUM(Revenue) AS TotalRevenue FROM Orders GROUP BY CustomerID ORDER BY TotalRevenue DESC; 3. Scenario: Creating a Monthly Sales Forecast in Power BI Approach: Load Historical Data: Import historical sales data. Data Model: Ensure proper relationships. Time Series Analysis: Use built-in Power BI forecasting features. Create measures for historical and forecasted sales. Visualization: Use a Line Chart to display historical and forecasted sales. Adjust Forecast Parameters: Customize the forecast length and confidence intervals. 4. Scenario: Updating a SQL Table with New Data Approach: Understand the Schema: Identify the table and columns to be updated. SQL Query: UPDATE Employees SET JobTitle = 'Senior Developer' WHERE EmployeeID = 1234; 5. Scenario: Creating a Custom KPI in Power BI Approach: Define KPI: Identify the key performance indicators. Create Measures: Define the KPI measure using DAX. Visualization: Use KPI Visual or Card Visual. Configure the target and actual values. Conditional Formatting: Apply conditional formatting based on the KPI thresholds. Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope it helps :)

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🚀 𝗧𝗼𝗽 𝟱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 | 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 😍 📈 Upgrade your career with in-demand tech skills & FREE certifications! 1️⃣ AI & ML – https://pdlink.in/3U3eZuq 2️⃣ Data Analytics – https://pdlink.in/4lp7hXQ 3️⃣ Cloud Computing – https://pdlink.in/3GtNJlO 4️⃣ Cyber Security – https://pdlink.in/4nHBuTh 5️⃣ More Courses – https://pdlink.in/3ImMFAB 🎓 100% FREE | Certificates Provided | Learn Anytime, Anywhere

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SQL Joins ✅

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Top 10 CSS Interview Questions 1. What is CSS and what are its key features? CSS (Cascading Style Sheets) is a stylesheet language used to describe the presentation of a document written in HTML or XML. Its key features include controlling layout, styling text, setting colors, spacing, and more, allowing for a separation of content and design for better maintainability and flexibility. 2. Explain the difference between inline, internal, and external CSS. - Inline CSS is applied directly within an HTML element using the style attribute. - Internal CSS is defined within a <style> tag inside the <head> section of an HTML document. - External CSS is linked to an HTML document via the <link> tag and is written in a separate .css file. 3. What is the CSS box model and what are its components? The CSS box model describes the rectangular boxes generated for elements in the document tree and consists of four components: - Content: The actual content of the element. - Padding: The space between the content and the border. - Border: The edge surrounding the padding. - Margin: The space outside the border that separates the element from others. 4. How do you center a block element horizontally using CSS? To center a block element horizontally, you can use the margin: auto; property. For example:

.center {
  width: 50%;
  margin: auto;
}

5. What are CSS selectors and what are the different types? CSS selectors are patterns used to select elements to apply styles. The different types include: - Universal selector (*) - Element selector (element) - Class selector (.class) - ID selector (#id) - Attribute selector ([attribute]) - Pseudo-class selector (:pseudo-class) - Pseudo-element selector (::pseudo-element) 6. Explain the difference between absolute, relative, fixed, and sticky positioning in CSS. - relative: The element is positioned relative to its normal position. - absolute: The element is positioned relative to its nearest positioned ancestor or the initial containing block if none exists. - fixed: The element is positioned relative to the viewport and does not move when the page is scrolled. - sticky: The element is treated as relative until a given offset position is met in the viewport, then it behaves as fixed. 7. What is Flexbox and how is it used in CSS? Flexbox (Flexible Box Layout) is a layout model that allows for more efficient arrangement of elements within a container. It is used to align and distribute space among items in a container, even when their size is unknown or dynamic. Flexbox is enabled by setting display: flex; on a container element. 8. How do you create a responsive design in CSS? Responsive design can be achieved using media queries, flexible grid layouts, and relative units like percentages, em, and rem. Media queries adjust styles based on the viewport's width, height, and other characteristics. For example:

@media (max-width: 600px) {
  .container {
    width: 100%;
  }
}

9. What are CSS preprocessors and name a few popular ones. CSS preprocessors extend CSS with variables, nested rules, and functions, making it more powerful and easier to maintain. Popular CSS preprocessors include: - Sass (Syntactically Awesome Style Sheets) - LESS (Leaner Style Sheets) - Stylus 10. How do you implement CSS animations? CSS animations are implemented using the @keyframes rule to define the animation and the animation property to apply it to an element. For example:

@keyframes example {
  from {background-color: red;}
  to {background-color: yellow;}
}

.element {
  animation: example 5s infinite;
}

Web Development Best Resources: https://topmate.io/coding/930165 ENJOY LEARNING 👍👍

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𝐒𝐭𝐚𝐫𝐭 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — 𝟏𝟎𝟎% 𝐅𝐫𝐞𝐞 & 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲😍 Want to dive into data analytics but don’t know where to start?🧑‍💻✨️ These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.📊📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/47oQD6f No prior experience needed — just curiosity✅️

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Want to become a Data Scientist? Here’s a quick roadmap with essential concepts: 1. Mathematics & Statistics Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning. Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance. Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization. 2. Programming Python or R: Choose a primary programming language for data science. Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning. R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization. SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets. 3. Data Wrangling & Preprocessing Data Cleaning: Handle missing values, outliers, duplicates, and data formatting. Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.). Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights. 4. Data Visualization Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data. Tableau or Power BI: Learn interactive visualization tools for building dashboards. Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders. 5. Machine Learning Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE). Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression. 6. Advanced Machine Learning & Deep Learning Neural Networks: Understand the basics of neural networks and backpropagation. Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data. Transfer Learning: Apply pre-trained models for specific use cases. Frameworks: Use TensorFlow Keras for building deep learning models. 7. Natural Language Processing (NLP) Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal. NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe). NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation. 8. Big Data Tools (Optional) Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing. 9. Data Science Workflows & Pipelines (Optional) ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring. Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform). 10. Model Validation & Tuning Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting. Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance. Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization. 11. Time Series Analysis Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting. Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting. 12. Experimentation & A/B Testing Experiment Design: Learn how to set up and analyze controlled experiments. A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes. ENJOY LEARNING 👍👍 #datascience

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📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱/𝗣𝘂𝗻𝗲 😍 Looking to become a Data Analyst? It’s one of the most in-demand roles in tech — and the best part? No coding required! 🔥 Learn Data Analytics with Real-time Projects ,Hands-on Tools ✨ Highlights: ✅ 100% Placement Support ✅ 500+ Hiring Partners ✅ Weekly Hiring Drives 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄:- 👇 🔹 Hyderabad :- https://pdlink.in/4kFhjn3 🔹 Pune:- https://pdlink.in/45p4GrC Hurry Up 🏃‍♂️! Limited seats are available.

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🚀 Become an Agentic AI Builder — Free 12‑Week Certification by Ready Tensor Ready Tensor’s Agentic AI Developer Certification is a free, project first 12‑week program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit. The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards. You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems. 👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/

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Breaking into Data Analytics doesn’t need to be complicated. If you’re just starting out, Here’s how to simplify your approach: Avoid: 🚫 Jumping into advanced tools like Hadoop or Spark before mastering the basics. 🚫 Focusing only on tools, not on business problem-solving. 🚫 Collecting certificates instead of solving real problems. 🚫 Thinking you need to know everything from SQL to machine learning right away. Instead: ✅ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau). ✅ Learn how to clean, explore, and interpret data to solve business questions. ✅ Understand core concepts like KPIs, dashboards, and business metrics. ✅ Pick real datasets and analyze them with clear goals and insights. ✅ Build a portfolio that shows you can translate data into decisions. React ❤️ for more

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𝟰 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗟𝗲𝗮𝗿𝗻 & 𝗘𝗮𝗿𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀😍 Upgrade Your Career with 100% FREE Learning Resources!📚✨️ From coding essentials to data analytics, programming foundations, and business insights — these handpicked free courses will help you gain practical, in-demand skills fast.🧑‍🎓📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4mCBGCa Perfect for beginners and professionals looking to upskill without spending a dime.✅️

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AI Tools

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𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍 Learn Fundamental Skills with Free Online Courses & Earn Certificates - AI - GenAI - Data Science - BigData - Python - UI/UX ,Cloud - Machine Learning - Cyber Security 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4ovjVWY Enroll for FREE & Get Certified 🎓

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One day or Day one. You decide. Data Science edition. 𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL. 𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench. 𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio. 𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on. 𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics. 𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course. 𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data. 𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart. 𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist. 𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.

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𝟓 𝐅𝐫𝐞𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐨𝐝𝐢𝐧𝐠😍 Want to Create AI Automations & Agents Without Writing a Single Line of Code?🧑‍💻 These 5 free YouTube tutorials will take you from complete beginner to automation expert in record time.🧑‍🎓✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4lhYwhn Just pure, actionable automation skills — for free.✅️

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TOP 10 SQL Concepts for Job Interview 1. Aggregate Functions (SUM/AVG) 2. Group By and Order By 3. JOINs (Inner/Left/Right) 4. Union and Union All 5. Date and Time processing 6. String processing 7. Window Functions (Partition by) 8. Subquery 9. View and Index 10. Common Table Expression (CTE) TOP 10 Statistics Concepts for Job Interview 1. Sampling 2. Experiments (A/B tests) 3. Descriptive Statistics 4. p-value 5. Probability Distributions 6. t-test 7. ANOVA 8. Correlation 9. Linear Regression 10. Logistics Regression TOP 10 Python Concepts for Job Interview 1. Reading data from file/table 2. Writing data to file/table 3. Data Types 4. Function 5. Data Preprocessing (numpy/pandas) 6. Data Visualisation (Matplotlib/seaborn/bokeh) 7. Machine Learning (sklearn) 8. Deep Learning (Tensorflow/Keras/PyTorch) 9. Distributed Processing (PySpark) 10. Functional and Object Oriented Programming

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𝗔𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀 AI is the future now & highly in demand 💼 Learn in-demand AI skills 📚 Beginner-friendly — No experience needed ✅ Get Certified & Boost Your Career 🎯 100% Free – Limited Time! 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 𝗡𝗼𝘄 👇:- https://pdlink.in/3U3eZuq 📌 Enroll today & start your AI journey!

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When to Use Which Programming Language? C ➝ OS Development, Embedded Systems, Game Engines C++ ➝ Game Dev, High-Performance Apps, Finance Java ➝ Enterprise Apps, Android, Backend C# ➝ Unity Games, Windows Apps Python ➝ AI/ML, Data, Automation, Web Dev JavaScript ➝ Frontend, Full-Stack, Web Games Golang ➝ Cloud Services, APIs, Networking Swift ➝ iOS/macOS Apps Kotlin ➝ Android, Backend PHP ➝ Web Dev (WordPress, Laravel) Ruby ➝ Web Dev (Rails), Prototypes Rust ➝ System Apps, Blockchain, HPC Lua ➝ Game Scripting (Roblox, WoW) R ➝ Stats, Data Science, Bioinformatics SQL ➝ Data Analysis, DB Management TypeScript ➝ Scalable Web Apps Node.js ➝ Backend, Real-Time Apps React ➝ Modern Web UIs Vue ➝ Lightweight SPAs Django ➝ AI/ML Backend, Web Dev Laravel ➝ Full-Stack PHP Blazor ➝ Web with .NET Spring Boot ➝ Microservices, Java Enterprise Ruby on Rails ➝ MVPs, Startups HTML/CSS ➝ UI/UX, Web Design Git ➝ Version Control Linux ➝ Server, Security, DevOps DevOps ➝ Infra Automation, CI/CD CI/CD ➝ Testing + Deployment Docker ➝ Containerization Kubernetes ➝ Cloud Orchestration Microservices ➝ Scalable Backends Selenium ➝ Web Testing Playwright ➝ Modern Web Automation Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17 ENJOY LEARNING 👍👍