Artificial Intelligence & ChatGPT Prompts
๐Unlock Your Coding Potential with ChatGPT ๐ Your Ultimate Guide to Ace Coding Interviews! ๐ป Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data
Show more๐ 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.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, 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.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โ๐Unlock Your Coding Potential with ChatGPT
๐ Your Ultimate Guide to Ace Coding Interviews!
๐ป 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.
-- 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 EngagementArtificial 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 thereโs 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.โถ๏ธContent: โฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟ 001 What is Deep Learning 002 Plan of Attack 003 The Neuron 004 The Activation Function 005 How do Neural Networks work 006 How do Neural Networks learn 007 Gradient Descent 008 Stochastic Gradient Descent 009 Back propagation โฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟ ๐๐ Deep Learning Complete Course
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