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Artificial Intelligence & ChatGPT Prompts

Artificial Intelligence & ChatGPT Prompts

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๐Ÿ”“Unlock Your Coding Potential with ChatGPT ๐Ÿš€ Your Ultimate Guide to Ace Coding Interviews! ๐Ÿ’ป Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

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๐Ÿ“ˆ 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.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, 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.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œ๐Ÿ”“Unlock Your Coding Potential with ChatGPT ๐Ÿš€ Your Ultimate Guide to Ace Coding Interviews! ๐Ÿ’ป Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_dataโ€

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
Posts Archive
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Data Scientist Roadmap | |-- 1. Basic Foundations |   |-- a. Mathematics |   |   |-- i. Linear Algebra |   |   |-- ii. Calculus |   |   |-- iii. Probability |   |   -- iv. Statistics |   | |   |-- b. Programming |   |   |-- i. Python |   |   |   |-- 1. Syntax and Basic Concepts |   |   |   |-- 2. Data Structures |   |   |   |-- 3. Control Structures |   |   |   |-- 4. Functions |   |   |   -- 5. Object-Oriented Programming |   |   | |   |   -- ii. R (optional, based on preference) |   | |   |-- c. Data Manipulation |   |   |-- i. Numpy (Python) |   |   |-- ii. Pandas (Python) |   |   -- iii. Dplyr (R) |   | |   -- d. Data Visualization |       |-- i. Matplotlib (Python) |       |-- ii. Seaborn (Python) |       -- iii. ggplot2 (R) | |-- 2. Data Exploration and Preprocessing |   |-- a. Exploratory Data Analysis (EDA) |   |-- b. Feature Engineering |   |-- c. Data Cleaning |   |-- d. Handling Missing Data |   -- e. Data Scaling and Normalization | |-- 3. Machine Learning |   |-- a. Supervised Learning |   |   |-- i. Regression |   |   |   |-- 1. Linear Regression |   |   |   -- 2. Polynomial Regression |   |   | |   |   -- ii. Classification |   |       |-- 1. Logistic Regression |   |       |-- 2. k-Nearest Neighbors |   |       |-- 3. Support Vector Machines |   |       |-- 4. Decision Trees |   |       -- 5. Random Forest |   | |   |-- b. Unsupervised Learning |   |   |-- i. Clustering |   |   |   |-- 1. K-means |   |   |   |-- 2. DBSCAN |   |   |   -- 3. Hierarchical Clustering |   |   | |   |   -- ii. Dimensionality Reduction |   |       |-- 1. Principal Component Analysis (PCA) |   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE) |   |       -- 3. Linear Discriminant Analysis (LDA) |   | |   |-- c. Reinforcement Learning |   |-- d. Model Evaluation and Validation |   |   |-- i. Cross-validation |   |   |-- ii. Hyperparameter Tuning |   |   -- iii. Model Selection |   | |   -- e. ML Libraries and Frameworks |       |-- i. Scikit-learn (Python) |       |-- ii. TensorFlow (Python) |       |-- iii. Keras (Python) |       -- iv. PyTorch (Python) | |-- 4. Deep Learning |   |-- a. Neural Networks |   |   |-- i. Perceptron |   |   -- ii. Multi-Layer Perceptron |   | |   |-- b. Convolutional Neural Networks (CNNs) |   |   |-- i. Image Classification |   |   |-- ii. Object Detection |   |   -- iii. Image Segmentation |   | |   |-- c. Recurrent Neural Networks (RNNs) |   |   |-- i. Sequence-to-Sequence Models |   |   |-- ii. Text Classification |   |   -- iii. Sentiment Analysis |   | |   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) |   |   |-- i. Time Series Forecasting |   |   -- ii. Language Modeling |   | |   -- e. Generative Adversarial Networks (GANs) |       |-- i. Image Synthesis |       |-- ii. Style Transfer |       -- iii. Data Augmentation | |-- 5. Big Data Technologies |   |-- a. Hadoop |   |   |-- i. HDFS |   |   -- ii. MapReduce |   | |   |-- b. Spark |   |   |-- i. RDDs |   |   |-- ii. DataFrames |   |   -- iii. MLlib |   | |   -- c. NoSQL Databases |       |-- i. MongoDB |       |-- ii. Cassandra |       |-- iii. HBase |       -- iv. Couchbase | |-- 6. Data Visualization and Reporting |   |-- a. Dashboarding Tools |   |   |-- i. Tableau |   |   |-- ii. Power BI |   |   |-- iii. Dash (Python) |   |   -- iv. Shiny (R) |   | |   |-- b. Storytelling with Data |   -- c. Effective Communication | |-- 7. Domain Knowledge and Soft Skills |   |-- a. Industry-specific Knowledge |   |-- b. Problem-solving |   |-- c. Communication Skills |   |-- d. Time Management |   -- e. Teamwork | -- 8. Staying Updated and Continuous Learning     |-- a. Online Courses     |-- b. Books and Research Papers     |-- c. Blogs and Podcasts     |-- d. Conferences and Workshops     `-- e. Networking and Community Engagement

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โ˜„๏ธ What is an Artificial Neural Networks? Artificial neural networks (ANN) give machines the ability to process data similar
โ˜„๏ธ What is an Artificial Neural Networks?
Artificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While thereโ€™s still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.
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Want to build your own AI agent? Here is EVERYTHING you need. One enthusiast has gathered all the resources to get started: ๏ฟฝ
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โ–ŽEssential Data Science Concepts Everyone Should Know: 1. Data Types and Structures: โ€ข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels) โ€ข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height) โ€ข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data) 2. Descriptive Statistics: โ€ข Measures of Central Tendency: Mean, Median, Mode (describing the typical value) โ€ข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data) โ€ข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution) 3. Probability and Statistics: โ€ข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns) โ€ข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing) โ€ข Confidence Intervals: Estimating the range of plausible values for a population parameter 4. Machine Learning: โ€ข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories) โ€ข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data) โ€ข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance) 5. Data Cleaning and Preprocessing: โ€ข Missing Value Handling: Imputation, Deletion (dealing with incomplete data) โ€ข Outlier Detection and Removal: Identifying and addressing extreme values โ€ข Feature Engineering: Creating new features from existing ones (e.g., combining variables) 6. Data Visualization: โ€ข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually) โ€ข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively) 7. Ethical Considerations in Data Science: โ€ข Data Privacy and Security: Protecting sensitive information โ€ข Bias and Fairness: Ensuring algorithms are unbiased and fair 8. Programming Languages and Tools: โ€ข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn โ€ข R: Statistical programming language with strong visualization capabilities โ€ข SQL: For querying and manipulating data in databases 9. Big Data and Cloud Computing: โ€ข Hadoop and Spark: Frameworks for processing massive datasets โ€ข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data) 10. Domain Expertise: โ€ข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis โ€ข Problem Framing: Defining the right questions and objectives for data-driven decision making Bonus: โ€ข Data Storytelling: Communicating insights and findings in a clear and engaging manner Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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