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🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Больше📈 Аналитический обзор Telegram-канала Artificial Intelligence
Канал Artificial Intelligence (@machinelearning_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 53 207 подписчиков, занимая 3 254 место в категории Образование и 7 029 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 53 207 подписчиков.
Согласно последним данным от 10 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 1 050, а за последние 24 часа — 35, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 5.80%. В первые 24 часа после публикации контент обычно набирает 1.68% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 086 просмотров. В течение первых суток публикация набирает 892 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 9.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, classification, layer, pattern, chatbot.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
Благодаря высокой частоте обновлений (последние данные получены 11 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
53 207
Подписчики
+3524 часа
+1927 дней
+1 05030 день
Архив постов
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Step 6: Advanced Topics in Computer Vision
Object Detection:
Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN).
YOLO (You Only Look Once).
SSD (Single Shot MultiBox Detector).
RetinaNet.
Anchor boxes and non-maximum suppression.
Image Segmentation:
Semantic segmentation (U-Net, SegNet).
Instance segmentation (Mask R-CNN).
Panoptic segmentation.
Fully Convolutional Networks (FCNs).
CRFs (Conditional Random Fields).
Artificial Intelligence
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Step 5: Deep Learning for Computer Vision
Convolutional Neural Networks (CNNs):
Convolutional layers.
Pooling layers.
Fully connected layers.
Activation functions (ReLU, Sigmoid, Tanh).
Batch normalization and dropout.
Advanced CNN Architectures:
AlexNet and VGGNet.
ResNet (Residual Networks).
Inception and GoogLeNet.
DenseNet (Densely Connected Networks).
MobileNet and EfficientNet.
Artificial Intelligence
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Step 4: Machine Learning for Computer Vision
Classical Machine Learning Techniques:
K-Nearest Neighbors (KNN).
Support Vector Machines (SVM).
Decision Trees and Random Forests.
Naive Bayes.
Clustering (K-means, DBSCAN).
Dimensionality Reduction:
Principal Component Analysis (PCA).
Linear Discriminant Analysis (LDA).
t-SNE (t-Distributed Stochastic Neighbor Embedding).
Independent Component Analysis (ICA).
Feature selection techniques.
Artificial Intelligence
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Step 3: Feature Extraction
Traditional Feature Detectors:
Edge detection (Sobel, Canny).
Corner detection (Harris, Shi-Tomasi).
Blob detection (LoG, DoG).
SIFT and SURF features.
ORB features.
Image Segmentation:
Thresholding.
Watershed algorithm.
Contours and shape detection.
Region growing.
Graph-based segmentation.
Artificial Intelligence
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Step 2: Basic Image Processing
Image Manipulation with OpenCV:
Reading, displaying, and saving images.
Basic operations (resizing, cropping, rotating).
Image filtering (blurring, sharpening, edge detection).
Handling image channels and color spaces.
Image Manipulation with PIL and Scikit-Image:
Image enhancement techniques.
Histogram equalization.
Geometric transformations.
Image segmentation (thresholding, watershed).
Artificial Intelligence
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Introduction to Computer Vision:
Understanding images and pixels.
Grayscale and color images.
Basic image processing operations.
Image formats and conversions.
Mathematics for Computer Vision:
Linear algebra (matrices, vectors, transformations).
Calculus (derivatives, gradients).
Probability and statistics (distributions, Bayes theorem).
Fourier transforms and convolutions.
Eigenvalues and eigenvectors.
Artificial Intelligence
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Before we start, what is computer vision and what do computer vision engineers do?
Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and videos.
Computer vision engineers develop algorithms and systems to automate tasks like image classification, object detection, and image segmentation, transforming visual data into actionable insights for various applications including healthcare, autonomous driving, and security.
Artificial Intelligence
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Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
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How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
I’ve completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
Coursera - Deep Learning by Andrew Ng
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
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The first channel in the world of Telegram is dedicated to helping students and programmers of artificial intelligence, machine learning and data science in obtaining data sets for their research.
https://t.me/DataPortfolio
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MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.
2023 lectures are starting in just one day, Jan 9th!
Link to register:
http://introtodeeplearning.com
MIT Introduction to Deep Learning The 2022 lectures can be found here:
https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
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Future Trends in Artificial Intelligence 👇👇
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.
Like for more ❤️
Artificial Intelligence
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You never got that kind of Physics in school
I'm hooked on this crazy professor's video experiments. He illustrates interesting and simple Physics
— Melted steel + liquid = ?
— What if an atomic bomb is detonated in the Mariana Trench?
— Attempts to drown an anvil in mercury
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AI is the next biggest skill to learn.
AI experts are earing up to $200000+ per year.
Here are 4 FREE courses from Google and Microsoft that most people don't know:
https://microsoft.github.io/AI-For-Beginners/?
https://www.cloudskillsboost.google/paths/118
https://www.deeplearning.ai/courses/ai-for-everyone/
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
More free resources: https://t.me/udacityfreecourse
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