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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
Больше📈 Аналитический обзор Telegram-канала Machine Learning & Artificial Intelligence | Data Science Free Courses
Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 66 762 подписчиков, занимая 2 446 место в категории Образование и 431 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 66 762 подписчиков.
Согласно последним данным от 25 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 519, а за последние 24 часа — 31, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 0.76%. В первые 24 часа после публикации контент обычно набирает 0.78% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 510 просмотров. В течение первых суток публикация набирает 524 просмотров.
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📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Благодаря высокой частоте обновлений (последние данные получены 26 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.
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Архив постов
Teaching with AI - 2024.pdf5.86 MB
Artificial Intelligence and Chatbots 101
👇👇
https://t.me/machinelearning_deeplearning/234
Top 10 Machine Learning Algorithms 👇👇
https://t.me/pythonspecialist/241
Quantum_machine_learning_what_quantum_computing_means_to_data_mining.pdf27.95 MB
Machine Learning, The Basics.pdf3.26 MB
FIVE TOP IMAGE RECOGNITION SOFTWARE 2024
Image recognition software is a computer program that uses deep learning algorithms and AI to identify objects, scenes, people, text, and activities in images and videos. The software works by extracting pixel features from an image, preparing labeled images for training, training the model to recognize images, and then using the trained model to identify objects in new images.
1. Meltwater Image Search: Meltwater's image recognition software offers social media monitoring capabilities with AI-powered computer vision models. It can search for images in non-verbal and non-textual content, detect demographics, celebrities, scenes, objects, and visual emotions. It also includes features like optical character recognition (OCR) and logo detection.
2. Google Reverse Image Search: Google's Reverse Image Search allows users to find more information about images by uploading them. It can identify objects in the image, provide similar images, and show websites with the same or similar images.
3. Clarifai: Clarifai's AI-powered computer vision software enables processing of images, videos, texts, and audio files. It can filter unwanted content, recommend relevant products, and manage unstructured data. Customizable AI models can be created for specific use cases.
4. Imagga: Imagga offers image recognition tools for sorting, organizing, and displaying images based on tags or categories. Its powerful API enables features such as product discoverability, facial recognition, and automated thumbnail generation.
5. Amazon Rekognition: Amazon Rekognition is a user-friendly image recognition software that provides insights on still images and videos. It offers features like activity recognition, face analysis, content moderation for unsafe and inappropriate content, and text detection for street names, image captions, and license plate numbers.
Machine Learning in Production
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Probability for data Science💡.pdf8.83 MB
Successful Algorithmic Trading
Michael L. Halls-Moore, 2015
Follow the Data Science and Machine Learning channel on WhatsApp: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
AI Engineer Roadmap
👇👇
https://t.me/generativeai_gpt/15
Learn Data Science in 2024
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join @datasciencefree for more
ENJOY LEARNING 👍👍
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Machine Learning with Python Free Course 👇👇
https://www.freecodecamp.org/learn/machine-learning-with-python/
©How fresher can get a job as a data scientist?©
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
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