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Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
Show more๐ Analytical overview of Telegram channel Machine Learning & Artificial Intelligence | Data Science Free Courses
Channel Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) in the English language segment is an active participant. Currently, the community unites 66 752 subscribers, ranking 2 450 in the Education category and 436 in the Malaysia region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 66 752 subscribers.
According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 534 over the last 30 days and by 42 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.75%. Within the first 24 hours after publication, content typically collects 0.79% reactions from the total number of subscribers.
- Post reach: On average, each post receives 502 views. Within the first day, a publication typically gains 524 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as sellerflash, waybienad, pricing, buybox, buyer.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โPerfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfunโ
Thanks to the high frequency of updates (latest data received on 25 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.
66 752
Subscribers
+4224 hours
+687 days
+53430 days
Posts Archive
Teaching with AI - 2024.pdf5.86 MB
Artificial Intelligence and Chatbots 101
๐๐
https://t.me/machinelearning_deeplearning/234
Top 10 Machine Learning Algorithms ๐๐
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
They predicted yesterday the DUMP of Bitcoin
Already in the channel published the dates of the next BTC PUMP!
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Probability for data Science๐ก.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
๐๐
https://t.me/generativeai_gpt/15
Learn Data Science in 2024
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
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.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ 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 donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
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 ๐๐
๐There is news that a young guy together with a team of the best programmers in Europe, invented a unique way to earn money, thanks to which everyone can earn from 300 dollars a day, having only a smartphone. ๐ฅ Together they created a Telegram channel, a closed community, where they tell about their strategy. Training is fast and easy, absolutely everyone will master it, you do not need special skills and knowledge. ๐The creator of the channel told us that he earns from 1000 dollars a day and it is absolutely real and available to everyone. He was able to change his life, drives expensive cars, travels, buys himself anything he wants and he assures that he can help to reach a high income to all those in need. โ๏ธNow there is a new recruitment for training in the team, all you need to do is to subscribe to his Telegram channel, hurry up, the number of places is limited.
๐ https://t.me/+m2VfkimmwUU5OWE1
Machine Learning with Python Free Course ๐๐
https://www.freecodecamp.org/learn/machine-learning-with-python/
ยฉHow fresher can get a job as a data scientist?ยฉ
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.
Credits: https://t.me/datasciencefun
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