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Artificial Intelligence

Artificial Intelligence

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πŸ”° Machine Learning & Artificial Intelligence Free Resources πŸ”° Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data

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πŸ“ˆ Analytical overview of Telegram channel Artificial Intelligence

Channel Artificial Intelligence (@machinelearning_deeplearning) in the English language segment is an active participant. Currently, the community unites 55 377 subscribers, ranking 3 050 in the Education category and 6 211 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 55 377 subscribers.

According to the latest data from 30 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 683 over the last 30 days and by 41 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 5.87%. Within the first 24 hours after publication, content typically collects 1.33% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 3 250 views. Within the first day, a publication typically gains 736 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 25.
  • Thematic interests: Content is focused on key topics such as learning, classification, layer, pattern, chatbot.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œπŸ”° Machine Learning & Artificial Intelligence Free Resources πŸ”° Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data”

Thanks to the high frequency of updates (latest data received on 31 August, 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 Education category.

55 377
Subscribers
+4124 hours
+1517 days
+68330 days
Posts Archive
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5 Algorithms you must know as a data scientist πŸ‘©β€πŸ’» πŸ§‘β€πŸ’» 1. Dimensionality Reduction - PCA, t-SNE, LDA 2. Regression models - Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression 3. Classification models - Binary classification- Logistic regression, SVM - Multiclass classification- One versus one, one versus many - Multilabel classification 4. Clustering models - K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models 5. Decision tree based models - CART model, ensemble models(XGBoost, LightGBM, CatBoost) Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/free4unow_backup Like if you need similar content πŸ˜„πŸ‘

Neural Networks and Deep Learning Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview: 1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer. Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs. Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation. 2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data. These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more. Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains. 3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs. Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers. Speech Recognition: Speech-to-text systems using deep neural networks. 4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges. LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning. 5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models. Join for more: https://t.me/machinelearning_deeplearning

AI as a life saver: 1. ChatGPT - thesis, essay, writing 2. Scite and perplexity - literature review 3. Consesus - latest research paper 4. Gemini - coding and technical 5. Claude AI - Analysis data, comparison data, literature review

Essential AI Concepts
Essential AI Concepts

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In Q3 earning call today, Google CEO said more than 25% of Google's new code is generated by AI
In Q3 earning call today, Google CEO said more than 25% of Google's new code is generated by AI

Top 5 key developments happening today in the AI and tech space. 1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI. 2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand. 3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools. 4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns. 5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.

Data Scientist: Focuses on data cleaning, preprocessing, and exploratory data analysis (EDA). Utilizes statistical modeling, hypothesis testing, and machine learning model development. AI Engineer: - Specializes in model deployment, integration, and optimizing model performance.

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Learn ChatGPT and Prompt Engineering Free.... πŸš€πŸ”₯ ChatGPT Quick Guide - Prompt Engineering, Plugins, and more!: In just 2 ho
Learn ChatGPT and Prompt Engineering Free.... πŸš€πŸ”₯ ChatGPT Quick Guide - Prompt Engineering, Plugins, and more!:  In just 2 hours supercharge your ChatGPT skills with plugins, the code interpreter, and prompt engineering! ➑️  https://bit.ly/4eFiY9H ChatGPT in 30 Minutes: NEW Prompt Engineering & AI Skills:  All-New ChatGPT Prompting Skills. Learn AI Vision, 'No Code' Programming, Data Analytics,   More. Practical Examples.. ➑️  https://bit.ly/3L3eFaF ChatGPT Prompt Engineering ( Free Course ):  Craft Captivating AI prompts: Free Prompt Engineering Course with Real-Life examples! ➑️  https://bit.ly/3W2IqhW

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Applications of Deep Learning
Applications of Deep Learning

You can use ChatGPT to make money online. Here are 10 prompts by ChatGPT 1. Develop Email Newsletters: Make interesting email
You can use ChatGPT to make money online. Here are 10 prompts by ChatGPT 1. Develop Email Newsletters: Make interesting email newsletters to keep audience updated and engaged. Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?" 2. Create Online Course Material: Make detailed and educational online course content. Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?" Read more......

Powerful Impacts of AI on the Job Market You Need to Know Artificial Intelligence is not a recent innovation. Even though its current application is highly groundbreaking, It has been transforming jobs for decades. In what ways did AI transform jobs in the early years? Initially, Artificial Intelligence and machine learning applied automation only to repetitive, manual tasks in industries such as manufacturing and retail. However, with the increasing maturity of AI, the tasks it performed and took over became progressively more complex, shifting from finance and other healthcare-related sectors where human judgment came into the picture. ....read full article

πŸ”΄ How to MASTER a programming language using ChatGPT: πŸ“Œ 1. Can you provide some tips and best practices for writing clean and efficient code in [lang]? 2. What are some commonly asked interview questions about [lang]? 3. What are the advanced topics to learn in [lang]? Explain them to me with code examples. 4. Give me some practice questions along with solutions for [concept] in [lang]. 5. What are some common mistakes that people make in [lang]? 6. Can you provide some tips and best practices for writing clean and efficient code in [lang]? 7. How can I optimize the performance of my code in [lang]? 8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts? 9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively? 10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues? 11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]? 12. How can I effectively collaborate with other developers in [lang] on a project? 13. What are some common data structures and algorithms that I should be familiar with in [lang]? How to Create Resume using ChatGPT πŸ‘‡πŸ‘‡ https://t.me/free4unow_backup/687 Master DSA πŸ‘‡πŸ‘‡ https://t.me/dsabooks/156 Like for more ❀️ #ai