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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 94 077 subscribers, ranking 1 547 in the Education category and 3 005 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 94 077 subscribers.
According to the latest data from 26 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 965 over the last 30 days and by 37 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.79%. 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 6 384 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 27 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.
94 077
Subscribers
+3724 hours
+2267 days
+96530 days
Posts Archive
94 084
For those of you who are new to Neural Networks, let me try to give you a brief overview.
Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:1. Structure: Neural networks have three main types of layers: - Input layer: Receives the initial data. - Hidden layers: Intermediate layers that process the input data through weighted connections. - Output layer: Produces the final output or prediction. 2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer. 3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves: - Forward pass: Calculating the output based on current weights. - Loss calculation: Comparing the output to the actual result using a loss function. - Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent. 4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns. 5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others. Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data. ENJOY LEARNING ππ
94 084
π±Machine Learning and Artificial intelligence
π±Building a Video Transcriber with Node.js and Google AI Speech-To-Text API
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Building a Video Transcriber with Node.js and Google AI Speech-To-Text API
π Author: Fikayo Adepoju
π° Level: Intermediate
β° Duration: 1h 9m
π Learn how to transcribe audio from video by integrating Node.js applications with the Google AI Speech-to-Text API.π Topics: Machine Transcription, Artificial Intelligence, Node.js π€ Join Machine Learning and Artificial intelligence for more courses
94 084
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94 084
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94 084
Repost from Linkedin Learning
Welcome to our Development Pack! π
If you're interested in web development, mobile development, machine learning, or even ChatGPT, you're in the right place.
You'll find channels for everything, starting with the famous Python π and JavaScript, and finishing with React, Next.js, AutoCAD, and SolidWorks. We also cover databases, Linux π§, ethical hacking, cybersecurity, finance and marketing, crypto tutorials, and many more. π
π± Development Pack
94 084
π±Machine Learning and Artificial intelligence
π±Machine Learning and AI Foundations: Causal Inference and Modeling
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Machine Learning and AI Foundations: Causal Inference and Modeling
π Author: Keith McCormick
π° Level: Advanced
β° Duration: 2h 51m
π Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.π Topics: Causal Inference, Machine Learning, Artificial Intelligence π€ Join Machine Learning and Artificial intelligence for more courses
94 084
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94 084
π±Machine Learning and Artificial intelligence
π±AI Pair Programming with GitHub Copilot X
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AI Pair Programming with GitHub Copilot X
π Author: Ronnie Sheer
π° Level: Advanced
β° Duration: 1h 23m
π Learn how to streamline software development workflows using AI pair programming with GitHub Copilot X.π Topics: Pair Programming, GitHub Copilot, Artificial Intelligence π€ Join Machine Learning and Artificial intelligence for more courses
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94 084
π±Machine Learning and Artificial intelligence
π±Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
94 084
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Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
π Author: Keith McCormick
π° Level: Intermediate
β° Duration: 2h 9m
π Learn best practices for how to produce explainable AI and interpretable machine learning solutions.π Topics: Machine Learning, Artificial Intelligence π€ Join Machine Learning and Artificial intelligence for more courses
