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
Ko'proq ko'rsatish📈 Telegram kanali AI and Machine Learning analitikasi
AI and Machine Learning (@machine_learning_courses) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 94 843 obunachidan iborat bo'lib, Taʼlim toifasida 1 535-o'rinni va Hindiston mintaqasida 3 046-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 94 843 obunachiga ega bo‘ldi.
25 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 795 ga, so‘nggi 24 soatda esa 11 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 9.41% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.49% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 8 925 marta ko‘riladi; birinchi sutkada odatda 2 362 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 15 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, llm, linkedin, linux, udemy kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 26 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
94 843
Obunachilar
+1124 soatlar
+807 kunlar
+79530 kunlar
Postlar arxiv
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💸 Understanding Popular ML Algorithms:
1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.
2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.
3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.
4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.
5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.
6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!
7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.
8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.
9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.
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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 👍👍
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📱Machine Learning and Artificial intelligence
📱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
🌐 Author: Fikayo Adepoju
🔰 Level: Intermediate
⏰ Duration: 1h 9m
🌀 Learn how to transcribe audio from video by integrating Node.js applications with the Google AI Speech-to-Text API.📗 Topics: Machine Transcription, Artificial Intelligence, Node.js 📤 Join Machine Learning and Artificial intelligence for more courses
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The Open Doors Olympiad is your gateway to a captivating realm of knowledge and academic excellence!
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Repost from Linkedin Learning
Welcome to our Development Pack! 🚀
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 🐍 and JavaScript, and finishing with React, Next.js, AutoCAD, and SolidWorks. We also cover databases, Linux 🐧, ethical hacking, cybersecurity, finance and marketing, crypto tutorials, and many more. 🌟
📱 Development Pack
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📱Machine Learning and Artificial intelligence
📱Machine Learning and AI Foundations: Causal Inference and Modeling
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🔅 Machine Learning and AI Foundations: Causal Inference and Modeling
🌐 Author: Keith McCormick
🔰 Level: Advanced
⏰ Duration: 2h 51m
🌀 Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.📗 Topics: Causal Inference, Machine Learning, Artificial Intelligence 📤 Join Machine Learning and Artificial intelligence for more courses
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Turn your voice into organized notes with EchoNote! 🎙️➡️📝
Got an idea? Just say it, and EchoNote transforms your thoughts into clear, structured notes in seconds.
✅ Plan tasks
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Just tap, talk, and let EchoNote handle the rest. You’ll love the simplicity! 😉📲
=> https://tglink.io/ef9c6d41b47a
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🔅 AI Pair Programming with GitHub Copilot X
🌐 Author: Ronnie Sheer
🔰 Level: Advanced
⏰ Duration: 1h 23m
🌀 Learn how to streamline software development workflows using AI pair programming with GitHub Copilot X.📗 Topics: Pair Programming, GitHub Copilot, Artificial Intelligence 📤 Join Machine Learning and Artificial intelligence for more courses
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📱Machine Learning and Artificial intelligence
📱Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
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🔅 Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
🌐 Author: Keith McCormick
🔰 Level: Intermediate
⏰ Duration: 2h 9m
🌀 Learn best practices for how to produce explainable AI and interpretable machine learning solutions.📗 Topics: Machine Learning, Artificial Intelligence 📤 Join Machine Learning and Artificial intelligence for more courses
