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Artificial Intelligence & ChatGPT Prompts

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

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📈 Аналитический обзор Telegram-канала Artificial Intelligence & ChatGPT Prompts

Канал Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 42 136 подписчиков, занимая 3 236 место в категории Технологии и приложения и 9 528 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 42 136 подписчиков.

Согласно последним данным от 14 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 208, а за последние 24 часа — 12, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.25%. В первые 24 часа после публикации контент обычно набирает 0.71% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 947 просмотров. В течение первых суток публикация набирает 300 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, algorithm, detection, llm, pattern.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
🔓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

Благодаря высокой частоте обновлений (последние данные получены 15 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

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Essential Python Libraries to build your career in Data Science 📊👇 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Seaborn: - Statistical data visualization built on top of Matplotlib. 5. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 6. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 7. PyTorch: - Deep learning library, particularly popular for neural network research. 8. SciPy: - Library for scientific and technical computing. 9. Statsmodels: - Statistical modeling and econometrics in Python. 10. NLTK (Natural Language Toolkit): - Tools for working with human language data (text). 11. Gensim: - Topic modeling and document similarity analysis. 12. Keras: - High-level neural networks API, running on top of TensorFlow. 13. Plotly: - Interactive graphing library for making interactive plots. 14. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. 15. OpenCV: - Library for computer vision tasks. As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch. Free Notes & Books to learn Data Science: https://t.me/datasciencefree Python Project Ideas: https://t.me/dsabooks/85 Best Resources to learn Python & Data Science 👇👇 Python Tutorial Data Science Course by Kaggle Machine Learning Course by Google Best Data Science & Machine Learning Resources Interview Process for Data Science Role at Amazon Python Interview Resources Join @free4unow_backup for more free courses Like for more ❤️ ENJOY LEARNING👍👍

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Machine Learning isn't easy! It’s the field that powers intelligent systems and predictive models. To truly master Machine Learning, focus on these key areas: 0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation. 1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training. 2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression. 3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE). 4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance. 5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models. 6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance. 7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs. 8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers. 9. Staying Updated with New Techniques: Machine learning evolves rapidly—keep up with emerging models, techniques, and research. Machine learning is about learning from data and improving models over time. 💡 Embrace the challenges of building algorithms, experimenting with data, and solving complex problems. ⏳ With time, practice, and persistence, you’ll develop the expertise to create systems that learn, predict, and adapt. Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊 #datascience

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SQL Interview Questions 1. How would you find duplicate records in SQL? 2.What are various types of SQL joins? 3.What is a trigger in SQL? 4.What are different DDL,DML commands in SQL? 5.What is difference between Delete, Drop and Truncate? 6.What is difference between Union and Union all? 7.Which command give Unique values? 8. What is the difference between Where and Having Clause? 9.Give the execution of keywords in SQL? 10. What is difference between IN and BETWEEN Operator? 11. What is primary and Foreign key? 12. What is an aggregate Functions? 13. What is the difference between Rank and Dense Rank? 14. List the ACID Properties and explain what they are? 15. What is the difference between % and _ in like operator? 16. What does CTE stands for? 17. What is database?what is DBMS?What is RDMS? 18.What is Alias in SQL? 19. What is Normalisation?Describe various form? 20. How do you sort the results of a query? 21. Explain the types of Window functions? 22. What is limit and offset? 23. What is candidate key? 24. Describe various types of Alter command? 25. What is Cartesian product? Like this post if you need more content like this ❤️

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