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 556 Technologies & Applications3 235...

📈 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 42 105 subscribers, ranking 3 235 in the Technologies & Applications category and 9 556 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 42 105 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 171 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.47%. Within the first 24 hours after publication, content typically collects 0.74% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 040 views. Within the first day, a publication typically gains 311 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 12 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.

42 105

Subscribers

-224 hours

+317 days

+17130 days

1 040

Post views

~ 31124 hours

~ 43848 hours

2.47%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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Today, let's move to the next topic of Artificial Intelligence Roadmap: AI Basics Part-2: AI vs Machine Learning vs Deep Learning Artificial Intelligence (AI) - The big umbrella - Goal: Make machines act intelligently - Includes rules, logic, learning systems - Example: A chess program with fixed rules (no learning, still AI) Machine Learning (ML) - Subset of AI - Systems learn from data, no hard-coded rules - How it works: - You give input and output data - Model finds patterns - Uses patterns for new data - Examples: - Predict house prices from past sales - Fraud detection from transaction history Deep Learning (DL) - Subset of machine learning - Uses neural networks with many layers - Handles complex data - Why it matters: - Works well with images, audio, text - Learns features automatically - Examples: - Face recognition - Speech recognition - Chatbots Simple Comparison - AI: The goal - Machine Learning: How systems learn - Deep Learning: Powerful learning using neural networks Real Product Mapping - Spam filter: AI system, machine learning model - Face unlock: AI system, deep learning model When Each is Used - Rule-based AI: Small, fixed logic - Machine Learning: Structured data, predictions - Deep Learning: Images, voice, large-scale text Takeaway - AI is the field - Machine learning is the engine - Deep learning is the heavy machinery Double Tap ♥️ For Part-3

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Today, let's start with the first topic of Artificial Intelligence Roadmap: AI Basics Part-1 Artificial intelligence means - Building systems that perform tasks that need human intelligence Core idea - You give data, rules, or goals - The system learns patterns - It makes decisions or predictions What AI systems do - See: Image recognition, face unlock on phones - Hear: Voice assistants, speech to text - Read: Spam filters, document classification - Decide: Credit approval, recommendation engines How AI works at a high level - Input: Data like text, images, numbers - Processing: Algorithms learn patterns - Output: Prediction, classification, or action Simple example - Email spam filter - Input: Email text - Learning: Patterns from past spam emails - Output: Spam or not spam Where you see AI in real life - Google search ranking results - Netflix recommending movies - Amazon product suggestions - Google Maps traffic prediction - Banks flagging fraud transactions What AI is not - Not magic - Not human thinking - Not always correct - It depends fully on data quality Types of tasks AI solves - Classification: Spam vs not spam - Regression: House price prediction - Clustering: Customer grouping - Recommendation: Products, videos - Forecasting: Sales, demand Why AI matters in products - Handles large data fast - Reduces manual work - Improves decision accuracy - Scales to millions of users Your takeaway - AI solves specific problems - Data drives everything - Models learn patterns, not meaning Double Tap ♥️ For Part-2

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𝗜𝗻𝗱𝗶𝗮’𝘀 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 | 𝗔𝗜 𝗜𝗺𝗽𝗮𝗰𝘁 𝗕𝘂𝗶𝗹𝗱𝗮𝘁𝗵𝗼𝗻😍 Participate in the national AI hackathon under the India AI Impact Summit 2026 Submission deadline: 5th February 2026 Grand Finale: 16th February 2026, New Delhi 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:- https://pdlink.in/4qQfAOM a flagship initiative of the Government of India 🇮🇳

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✅ Complete Roadmap to Master Artificial Intelligence in 3 Months Month 1: Foundations • Week 1: AI basics – What artificial intelligence is – AI vs machine learning vs deep learning – Real business use cases Outcome: You know where AI fits in real products. • Week 2: Math and logic essentials – Linear algebra basics, vectors, matrices – Probability and statistics basics – Cost functions and optimization idea Outcome: You understand how models learn. • Week 3: Python for AI – Python syntax for analysis – NumPy arrays and operations – Pandas for data handling Outcome: You work with data confidently. • Week 4: Data preparation – Data cleaning and preprocessing – Handling missing values and outliers – Feature selection basics Outcome: Your data is model ready. Month 2: Machine Learning Core • Week 5: Supervised learning – Linear and logistic regression – Decision trees and random forest – Model evaluation, accuracy, precision, recall Outcome: You build prediction models. • Week 6: Unsupervised learning – K-means clustering – Hierarchical clustering – PCA with real examples Outcome: You find patterns in data. • Week 7: Model improvement – Overfitting and underfitting – Cross validation – Hyperparameter tuning Outcome: Your models perform better. • Week 8: Intro to deep learning – Neural network basics – Activation functions – Backpropagation concept Outcome: You understand how deep models work. Month 3: Applied AI and Job Prep • Week 9: Deep learning tools – TensorFlow or PyTorch basics – Build a simple neural network – Train and test models Outcome: You build neural models. • Week 10: Real world AI project – Choose use case, spam detection or sales prediction – Data prep, model training, evaluation – Simple deployment demo Outcome: One strong AI project. • Week 11: Interview preparation – Machine learning theory questions – Model selection questions – Project explanation flow Outcome: You answer with clarity. • Week 12: Resume and practice – AI focused resume – GitHub with notebooks and projects – Daily problem solving Outcome: You are AI job ready. Practice platforms: Kaggle, Google Colab, Scikit-learn docs Double Tap ♥️ For Detailed Explanation of Each Topic

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Top free Data Science resources 1. CS109 Data Science http://cs109.github.io/2015/pages/videos.html 2. Machine Learning with Python https://www.freecodecamp.org/learn/machine-learning-with-python/ 3. Learning From Data from California Institute of Technology http://work.caltech.edu/telecourse 4. Mathematics for Machine Learning by University of California, Berkeley https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI 5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY 6. Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM 7. CS 221 ― Artificial Intelligence https://stanford.edu/~shervine/teaching/cs-221/ 8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf 9. Python for Data Analysis by Boston University https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx 10. Data Mining bu University of Buffalo https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE Credits: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

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✅ Where to Apply for Web Development Jobs 💻🌐 Here’s a list of the best platforms to find web dev jobs, internships, and freelance gigs: 🔹 Job Portals (Full-time/Internships) 1. LinkedIn – Top platform for tech hiring 2. Indeed – Good for local & remote jobs 3. Glassdoor – Job search + company reviews 4. Naukri.com – Popular in India 5. Monster – Global listings 6. Internshala – Internships & fresher roles 🔹 Tech-Specific Platforms 1. Hirect App – Direct chat with startup founders/recruiters 2. AngelList / Wellfound – Startup jobs (remote/flexible) 3. Stack Overflow Jobs – Developer-focused listings 4. Turing / Toptal – Remote global jobs (for skilled devs) 🔹 Freelancing Platforms 1. Upwork – Projects from all industries 2. Fiverr – Set your own gigs (great for beginners) 3. Freelancer.com – Bidding-based freelance jobs 4. PeoplePerHour – Short-term dev projects 🔹 Social Media Platforms There are many WhatsApp & Telegram channels which post daily job updates. Here are some of the most popular job channels: Telegram channels: https://t.me/getjobss https://t.me/FAANGJob https://t.me/internshiptojobs https://t.me/jobs_us_uk WhatsApp Channels: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R https://whatsapp.com/channel/0029Vb4n3QZFy72478wwQp3n https://whatsapp.com/channel/0029VbAOss8EKyZK7GryN63V https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E https://whatsapp.com/channel/0029Vb8pF9b65yDKZxIAy83b https://whatsapp.com/channel/0029Vb9CzaNCcW4yxgR1jX3S 🔹 Others Worth Exploring - Remote OK / We Work Remotely – Remote jobs - Jobspresso / Remotive – Remote tech-focused roles - Hashnode / Dev.to – Community + job listings 💡 Tip: Always keep your LinkedIn & GitHub updated. Many recruiters search there directly! 👍 Tap ❤️ if you found this helpful!

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⌨️ Benefits of learning Python Programming 1. Web Development: Python frameworks like Django and Flask are popular for building dynamic websites and web applications. 2. Data Analysis: Python has powerful libraries like Pandas and NumPy for data manipulation and analysis, making it widely used in data science and analytic. 3. Machine Learning: Python's libraries such as TensorFlow, Keras, and Scikit-learn are extensively used for implementing machine learning algorithms and building predictive models. 4. Artificial Intelligence: Python is commonly used in AI development due to its simplicity and extensive libraries for tasks like natural language processing, image recognition, and neural network implementation. 5. Cybersecurity: Python is utilized for tasks such as penetration testing, network scanning, and creating security tools due to its versatility and ease of use. 6. Game Development: Python, along with libraries like Pygame, is used for developing games, prototyping game mechanics, and creating game scripts. 7. Automation: Python's simplicity and versatility make it ideal for automating repetitive tasks, such as scripting, data scraping, and process automation.

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