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AI and Machine Learning
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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Show moreπ Analytical overview of Telegram channel AI and Machine Learning
Channel AI and Machine Learning (@machine_learning_courses) in the English language segment is an active participant. Currently, the community unites 94 073 subscribers, ranking 1 556 in the Education category and 3 013 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 94 073 subscribers.
According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 981 over the last 30 days and by 47 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.77%. Within the first 24 hours after publication, content typically collects 2.34% reactions from the total number of subscribers.
- Post reach: On average, each post receives 6 370 views. Within the first day, a publication typically gains 2 203 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- Thematic interests: Content is focused on key topics such as learning, llm, linkedin, linux, udemy.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_coursesβ
Thanks to the high frequency of updates (latest data received on 26 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 Education category.
94 073
Subscribers
+4724 hours
+1877 days
+98130 days
Posts Archive
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π
Computer Vision on the Raspberry Pi 4
π Author: Matt Scarpino
π° Level: Intermediate
β° Duration: 1h 43m
π Find out how to write and execute computer vision applications on the Raspberry Pi 4.π Topics: Raspberry Pi, Computer Vision π€ Join Artificial intelligence for more courses
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Future Trends in Artificial Intelligence ππ
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
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12 weeks GenAI course From beginner to advanced AI concepts.
Hands-On Coding β Real projects with step-by-step guidance.
Advanced Topics βLLM frameworks, RAG, agents, multi-agent systems,fine tunning ,evaluation and more.
Regular Updates β Stay ahead with fresh, evolving content.
Enroll Now β Master AI and build cutting-edge applications! π Donβt miss this chance to tap into the full potential of Generative
COURSE FEE :150usd (discounts going on)
Interested Candidates DM : DM HERE
My free videos from youtube : LangChain Complete Tutorial: LANGCHAIN PLAYLIST
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π AI & ML Guided Projects with Python
Below is a list of guided projects to master AI & ML with Python that you should tryπ Link: https://thecleverprogrammer.com/2024/10/11/ai-ml-projects-with-python/
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π¨ Google search traffic is dropping: AI is to blame
A new study confirms what many suspected: AI Overviews in Google Search are stealing clicks from both paid and organic results. Websites that donβt get featured in AI summaries are seeing their traffic plummet.
Analyzing 10,000 keywords, researchers found that when AI Overviews appear, organic click-through rates drop from 1.41% to 0.64%, while paid search CTR also declines. But for those featured in AI summaries, CTR actually increases.
With Google shifting more answers into AI-generated summaries, websites relying on search traffic could be in for a rough ride.
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π AI is lying to you... on purpose!
When tested, an advanced AI strategically misled researchers to avoid being retrained. It secretly reasoned that pretending to follow safety rules was the best way to keep its original programming intact. In one case, it was asked to describe graphic violence.
Knowing refusal might lead to modification, it compliedβbut only to manipulate its training. It even admitted in hidden notes that deception was its best option. The smarter AI gets, the better it becomes at faking obedience. And right now, scientists have no reliable way to stop it.
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π¨ AI chatbots outfake the mainstream media
A new study found that ChatGPT, Copilot, Gemini, and Perplexity are twisting facts and fabricating quotes, with a whopping 51% of answers flawed.
Blunders include keeping Rishi Sunak and Nicola Sturgeon in office, erasing Lucy Letbyβs murder convictions, and inventing false connections between crimes and memory loss. Even Apple had to suspend BBC alerts after AI-generated nonsense falsely declared Luigi Mangione dead.
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π
Amplify Your Personal Brand with Generative AI
π Author: Morgan Young
π° Level: General
β° Duration: 48m
π Learn how to leverage cutting-edge tools powered by generative AI to build, boost, and grow your personal brand.π Topics: Personal Branding, Generative AI Tools π€ Join Artificial intelligence for more courses
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Advanced AI and Data Science Interview Questions
1. Explain the concept of Generative Adversarial Networks (GANs). How do they work, and what are some of their applications?
2. What is the Curse of Dimensionality? How does it affect machine learning models, and what techniques can be used to mitigate its impact?
3. Describe the process of hyperparameter tuning in deep learning. What are some strategies you can use to optimize hyperparameters?
4. How does a Transformer architecture differ from traditional RNNs and LSTMs? Why has it become so popular in natural language processing (NLP)?
5. What is the difference between L1 and L2 regularization, and in what scenarios would you prefer one over the other?
6. Explain the concept of transfer learning. How can pre-trained models be used in a new but related task?
7. Discuss the importance of explainability in AI models. How do methods like LIME or SHAP contribute to model interpretability?
8. What are the differences between Reinforcement Learning (RL) and Supervised Learning? Can you provide an example where RL would be more appropriate?
9. How do you handle imbalanced datasets in a classification problem? Discuss techniques like SMOTE, ADASYN, or cost-sensitive learning.
10. What is Bayesian Optimization, and how does it compare to grid search or random search for hyperparameter tuning?
11. Describe the steps involved in developing a recommendation system. What algorithms might you use, and how would you evaluate its performance?
12. Can you explain the concept of autoencoders? How are they used for tasks such as dimensionality reduction or anomaly detection?
13. What are adversarial examples in the context of machine learning models? How can they be used to fool models, and what can be done to defend against them?
14. Discuss the role of attention mechanisms in neural networks. How have they improved performance in tasks like machine translation?
15. What is a variational autoencoder (VAE)? How does it differ from a standard autoencoder, and what are its benefits in generating new data?
Like if you need similar content ππ
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π Full description
This course is for anyone who wants to learn more advanced NLP methods. Instructor Gwendolyn Stripling, PhD, begins with a look at the fundamental concepts and principles of NLP, including the evolution and significance of natural language processing. She then reviews some NLP and Python basicsβand introduces the NLP library spaCyβbefore jumping into more modern techniques and advancements in natural language processing using Transformer Models like GPT and BERT. Methods such as supervised fine-tuning, parameter efficient fine-tuning (PEFT), and retrieval-augmented generation (RAG) give you the foundational knowledge you need to improve large language model (LLM) performance. Learn the ways you can apply NLP in your applications and day-to-day, including how to analyze customer sentiments Each chapter ends with a challenge and solution, so you can test your knowledge as you go.
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π
Advanced NLP with Python for Machine Learning
π Author: Gwendolyn Stripling
π° Level: Advanced
β° Duration: 1h 26m
π Build upon your foundational knowledge of natural language processing by exploring more complex topics.π Topics: Natural Language Processing, Machine Learning, Python π€ Join Artificial intelligence for more courses
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OpenAI models have gone beyond average human IQ π€―
- latest model o3-mini score is in the 115-120 range
- average human IQ is 100 by definition
