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Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

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Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data

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๐Ÿ“ˆ Analytical overview of Telegram channel Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Channel Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books (@programming_guide) in the English language segment is an active participant. Currently, the community unites 56 107 subscribers, ranking 2 375 in the Technologies & Applications category and 6 527 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 56 107 subscribers.

According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 105 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 2.63%. Within the first 24 hours after publication, content typically collects 0.84% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 473 views. Within the first day, a publication typically gains 470 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 algorithm, structure, stack, javascript, programming.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œEverything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_dataโ€

Thanks to the high frequency of updates (latest data received on 10 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.

56 107
Subscribers
+1224 hours
+527 days
+10530 days
Posts Archive
๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ - ๐—˜๐—ฟ๐—ผ๐—น๐—น ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ˜ Industry-approved Certifications to
๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ - ๐—˜๐—ฟ๐—ผ๐—น๐—น ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ˜  Industry-approved Certifications to enhance employability ๐—”๐—œ & ๐— ๐—Ÿ :- https://pdlink.in/4nwV054 ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ :-https://pdlink.in/4l3nFx0 ๐—–๐—น๐—ผ๐˜‚๐—ฑ ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ถ๐—ป๐—ด :- https://pdlink.in/4lteAgN ๐—–๐˜†๐—ฏ๐—ฒ๐—ฟ ๐—ฆ๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† :- https://pdlink.in/3ZLHHmW ๐—ข๐˜๐—ต๐—ฒ๐—ฟ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ :-https://pdlink.in/3G5G9O4 ๐— ๐—ผ๐—ฐ๐—ธ ๐—”๐˜€๐˜€๐—ฒ๐˜€๐˜€๐—บ๐—ฒ๐—ป๐˜:- https://pdlink.in/4kan6A9 Get the Govt. of India Incentives on course completion๐ŸŽ“

๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ & ๐—ฆ๐˜๐—ฎ๐—ป๐—ณ๐—ผ๐—ฟ๐—ฑ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ง๐—ต๐—ฎ๐˜ ๐—ช๐—ถ๐—น๐—น ๐—”๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๏ฟฝ
๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ & ๐—ฆ๐˜๐—ฎ๐—ป๐—ณ๐—ผ๐—ฟ๐—ฑ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ง๐—ต๐—ฎ๐˜ ๐—ช๐—ถ๐—น๐—น ๐—”๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ๐Ÿ˜ I failed my first data interview โ€” and hereโ€™s why:โฌ‡๏ธ โŒ No structured learning โŒ No real projects โŒ Just random YouTube tutorials and half-read blogs If this sounds like you, donโ€™t repeat my mistakeโœจ๏ธ Recruiters want proof of skills, not just buzzwords๐Ÿ“Š ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4ka1ZOl All The Best ๐ŸŽŠ

Machine Learning โ€“ Essential Concepts ๐Ÿš€ 1๏ธโƒฃ Types of Machine Learning Supervised Learning โ€“ Uses labeled data to train models. Examples: Linear Regression, Decision Trees, Random Forest, SVM Unsupervised Learning โ€“ Identifies patterns in unlabeled data. Examples: Clustering (K-Means, DBSCAN), PCA Reinforcement Learning โ€“ Models learn through rewards and penalties. Examples: Q-Learning, Deep Q Networks 2๏ธโƒฃ Key Algorithms Regression โ€“ Predicts continuous values (Linear Regression, Ridge, Lasso). Classification โ€“ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes). Clustering โ€“ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN). Dimensionality Reduction โ€“ Reduces the number of features (PCA, t-SNE, LDA). 3๏ธโƒฃ Model Training & Evaluation Train-Test Split โ€“ Dividing data into training and testing sets. Cross-Validation โ€“ Splitting data multiple times for better accuracy. Metrics โ€“ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC. 4๏ธโƒฃ Feature Engineering Handling missing data (mean imputation, dropna()). Encoding categorical variables (One-Hot Encoding, Label Encoding). Feature Scaling (Normalization, Standardization). 5๏ธโƒฃ Overfitting & Underfitting Overfitting โ€“ Model learns noise, performs well on training but poorly on test data. Underfitting โ€“ Model is too simple and fails to capture patterns. Solution: Regularization (L1, L2), Hyperparameter Tuning. 6๏ธโƒฃ Ensemble Learning Combining multiple models to improve performance. Bagging (Random Forest) Boosting (XGBoost, Gradient Boosting, AdaBoost) 7๏ธโƒฃ Deep Learning Basics Neural Networks (ANN, CNN, RNN). Activation Functions (ReLU, Sigmoid, Tanh). Backpropagation & Gradient Descent. 8๏ธโƒฃ Model Deployment Deploy models using Flask, FastAPI, or Streamlit. Model versioning with MLflow. Cloud deployment (AWS SageMaker, Google Vertex AI). Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

๐ŸŽ“ ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ Access 1000+ free courses in top domains like: ๐Ÿ”น AI &
๐ŸŽ“ ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ Access 1000+ free courses in top domains like: ๐Ÿ”น AI & GenAI ๐Ÿ”น Data Science ๐Ÿ”น Digital Marketing ๐Ÿ”น UI/UX Design & more โœ… Learn from top faculty & industry experts โœ… Get industry-recognized certificates โœ… Boost your CV with valuable credentials ๐Ÿ“Œ Start learning today โ€” itโ€™s 100% free! ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:-    https://pdlink.in/4dJ27Ta   Enroll For FREE & Get Certified ๐ŸŽ“

๐Ÿงฟ Essential React Hooks Part 2 Each hook addresses specific performance, state management, or accessibility needs within Rea
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๐Ÿงฟ Essential React Hooks Part 2
Each hook addresses specific performance, state management, or accessibility needs within React components, allowing for efficient and organized code structure.

๐Ÿงฟ Essential React Hooks Part 1 Each hook addresses specific performance, state management, or accessibility needs within Rea
+8
๐Ÿงฟ Essential React Hooks Part 1
Each hook addresses specific performance, state management, or accessibility needs within React components, allowing for efficient and organized code structure.

๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ ๐—ฏ๐˜† ๐—›๐—ฎ๐—ฟ๐˜ƒ๐—ฎ๐—ฟ๐—ฑ, ๐—œ๐—•๐— , ๐—จ๐—ฑ๐—ฎ๐—ฐ๐—ถ๐˜๐˜† & ๐— ๐—ผ๐—ฟ๐—ฒ๐Ÿ˜ Lo
๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ ๐—ฏ๐˜† ๐—›๐—ฎ๐—ฟ๐˜ƒ๐—ฎ๐—ฟ๐—ฑ, ๐—œ๐—•๐— , ๐—จ๐—ฑ๐—ฎ๐—ฐ๐—ถ๐˜๐˜† & ๐— ๐—ผ๐—ฟ๐—ฒ๐Ÿ˜ Looking to learn Python from scratchโ€”without spending a rupee? ๐Ÿ’ป Offered by trusted platforms like Harvard University, IBM, Udacity, freeCodeCamp, and OpenClassrooms, each course is self-paced, easy to follow, and includes a certificate of completion๐Ÿ”ฅ๐Ÿ‘จโ€๐ŸŽ“ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3HNeyBQ Kickstart your careerโœ…๏ธ

๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฒ๐—บ๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—œ๐—ป ๐—ฃ๐˜‚๐—ป๐—ฒ ๐Ÿ˜ ๐Ÿ“Š โ€œData Analystโ€ is one of the hottest
๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฒ๐—บ๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—œ๐—ป ๐—ฃ๐˜‚๐—ป๐—ฒ ๐Ÿ˜ ๐Ÿ“Š โ€œData Analystโ€ is one of the hottest careers in tech โ€” and guess what? NO coding needed! Learn Data Analytics in Pune with Hands-on Training, Industry Projects, and 100% Placement Assistance. ๐—›๐—ถ๐—ด๐—ต๐—น๐—ถ๐—ด๐—ต๐˜๐—ฒ๐˜€ :- - 100% Placement Assistance - 500+ Hiring Partners  - Weekly Hiring Drives  ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฒ๐—ฟ ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ‘‡:- https://pdlink.in/45p4GrC Location:- Acciojob Skill Centre ,Baner, Pune

Theoretical Questions for Coding Interviews on Basic Data Structures 1. What is a Data Structure? A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees. 2. What is an Array? An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations. 3. What is a Linked List? A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically. 4. What is a Stack? A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element). 5. What is a Queue? A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element). 6. What is a Binary Tree? A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting. 7. What is the difference between an array and a linked list? Array: Fixed size, elements stored in contiguous memory. Linked List: Dynamic size, elements stored non-contiguously, each node points to the next. 8. What is the time complexity for accessing an element in an array vs. a linked list? Array: O(1) for direct access by index. Linked List: O(n) for access, as you must traverse the list from the start to find an element. 9. What is the time complexity for inserting or deleting an element in an array vs. a linked list? Array: Insertion/Deletion at the end: O(1). Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted. Linked List: Insertion/Deletion at the beginning: O(1). Insertion/Deletion in the middle or end: O(n), as you need to traverse the list. 10. What is a HashMap (or Dictionary)? A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1). Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐—›๐—ฎ๐—ฟ๐˜ƒ๐—ฎ๐—ฟ๐—ฑ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ ๐—ถ๐—ป ๐—ง๐—ฒ๐—ฐ๐—ต (๐—ก๐—ผ ๐—˜๐˜…๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ก๐—ฒ๐—ฒ๐—ฑ๐—ฒ๐—ฑ!)๐Ÿ˜
๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐—›๐—ฎ๐—ฟ๐˜ƒ๐—ฎ๐—ฟ๐—ฑ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ ๐—ถ๐—ป ๐—ง๐—ฒ๐—ฐ๐—ต (๐—ก๐—ผ ๐—˜๐˜…๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ก๐—ฒ๐—ฒ๐—ฑ๐—ฒ๐—ฑ!)๐Ÿ˜ Dreaming of learning from Harvard โ€” without spending a rupee?๐Ÿ’ฐ Youโ€™re in luck! These 4 beginner-friendly courses from Harvard University are completely free, self-paced, & beginner-approved๐Ÿ‘จโ€๐ŸŽ“๐Ÿ“Œ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/44pDCYd Taught by world-class professors!โœ…๏ธ

Iโ€™ve never met an awesome software developer who: - Thought learning new frameworks was a waste. - Avoided refactoring becaus
Iโ€™ve never met an awesome software developer who: - Thought learning new frameworks was a waste. - Avoided refactoring because โ€œit already works.โ€ - Avoided debugging because it was frustrating. - Never deleted code they once proudly wrote. - Never pushed code that broke in production. - Stuck to one programming language forever. - Stopped learning after getting their first job. - Didnโ€™t rewrite their code later. - Only worked on projects that felt safe. - Refused to ask questions when stuck. Great developers arenโ€™t perfect. They take risks. They make mistakes. They debug endlessly. They make wrong estimates. But during all that, They learn. And thatโ€™s exactly why they grow. Keep that in mind

๐Ÿฑ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ + ๐—Ÿ๐—ถ๐—ป๐—ธ๐—ฒ๐—ฑ๐—œ๐—ป ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—˜๐˜€๐˜€๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜๐—ผ ๐—•๐—ผ๐—ผ๐˜€๏ฟฝ
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