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Data science/ML/AI
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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
Show more📈 Analytical overview of Telegram channel Data science/ML/AI
Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 684 subscribers, ranking 9 384 in the Technologies & Applications category and 31 551 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 684 subscribers.
According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 150 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 8.13%. Within the first 24 hours after publication, content typically collects 2.20% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 112 views. Within the first day, a publication typically gains 301 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
Thanks to the high frequency of updates (latest data received on 12 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 Technologies & Applications category.
13 684
Subscribers
+1124 hours
+227 days
+15030 days
Posts Archive
13 684
Implementing DBSCAN in Python
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based unsupervised learning algorithm. It computes nearest neighbor graphs to find arbitrary-shaped clusters and outliers. Whereas the K-means clustering generates spherical-shaped clusters.
Learn more about working with it in this article
Link
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Hello Dear😊!!!
Have you heard of The Python For Machine Learning International Bootcamp coming up on the 12th of September?
Link: Click Me
If you haven't, Global AI Hub is organizing a FREE ONE-MONTH INTENSIVE boot camp on python for machine learning.
This is a chance to improve yourselves in subjects such as Python😍, #machinelearning😍, #datascience😍, and #deeplearning😍!!!
In addition, you will be able to develop your portfolios ☺️ with the project work😃 that you will do from scratch under the guidance of mentors!!!😁
Does this look very interesting to you, click the link in this post to register
Link: Click Me
DEADLINE😱😱 : 7th September 2022
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Efficient Python Tricks and Tools for
Data Scientists - By Khuyen Tra
GithubRepo : https://github.com/khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists
Stars ⭐️: 675
Forked By: 202
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Machine Learning Engineer Learning Path
Course Link
Hey there!!
Check out this Machine Learning Course from Google.
Here's what you can learn from it.
👌A Tour of Google Cloud Hands-on Labs
👌Google Cloud Big Data and Machine Learning Fundamentals
👌How Google Does Machine Learning
👌Launching into Machine Learning
👌TensorFlow on Google Cloud
👌Feature Engineering
👌Machine Learning in the Enterprise
👌Production Machine Learning Systems
😃And a lot of interesting machine learning topics
Course Link
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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ARTIFICIAL INTELLIGENCE FOR BEGINNERS
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence.
In this curriculum, you will learn:
⭐️Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
⭐️Neural Networks and Deep Learning, which are at the core of modern AI. It illustrates the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
⭐️Neural Architectures for working with images and text. It covers recent models but may lack a little bit on the state-of-the-art.
⭐️Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.
Course Link
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 684
Types of Regression Analysis in Machine Learning
If you are looking to dive deeper into Regression Analysis for Machine Learning and understand how to choose the right type of regression analysis model for your project, here's an article that can help.
Link: https://www.projectpro.io/article/types-of-regression-analysis-in-machine-learning/410
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👉Here's an amazing self explanatory infographics that depicts the SQL Join clause with each category quite easily.
📍Types of joins used very often includes -
✔️LEFT JOIN - All data from the left table but common data from the right table
✔️RIGHT JOIN - All data from right table and common data from the left table
✔️INNER JOIN - Only common data from both the tables
✔️OUTER JOIN - All the data from both the tables keeping null values with no common keys
✔️UNION - Stack table data on top of one another
✔️CROSS JOIN - All possible combinations of data from both the tables
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Image Recognition for Beginners using CNN in R Studio
Rating ⭐️: 4.3 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 76,420
Created by: Start-Tech Academy
What you will learn:
⭐️Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
⭐️Build an end-to-end Image recognition project in R
⭐️Learn usage of Keras and Tensorflow libraries
⭐️Use Artificial Neural Networks (ANN) to make predictions
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 684
PYTHON FOR MACHINE LEARNING COURSE
This course is brought to you by AI Business School with the contribution of Samsung SDS and Global AI Hub for free.
In this course, you’ll learn everything you need to know to:
😃 solve real-life problems with Python and transition to machine learning and AI.
😃Work on complex programming projects efficiently, to get the data in the shape that your program needs,
😃Learn how to prepare and process your data to understand the story it holds.
😃A certificate of completion
Course Link: Click Me!!!
13 684
Artificial Neural Networks (ANN) with Keras in Python and R
Rating ⭐️: 4.5 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 150,528
Created by: Start-Tech Academy
🔗 Course link
Linear Regression and Logistic Regression in Python
Rating ⭐️: 4.6 out of 5
Duration ⏰: 7.5 hours on-demand video
Students 👨🏫: 50,422
Created by: Start-Tech Academy
🔗 Course link
Support Vector Machines in Python: SVM Concepts & Code
Rating ⭐️: 4.7 out of 5
Duration ⏰: 6 hours on-demand video
Students 👨🏫: 80,685
Created by: Start-Tech Academy
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 684
A WELL CONCISED INTRODUCTION TO REINFORCEMENT LEARNING
Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. This article will guide you through understanding RL and it's applications.
Link: Read Me👀
What you will learn:
👌How RL Works
👌Examples of RL
👌Benefits of RL
👌Challenges of RL
👌Future of RL
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Harvard University Data Science Course 2021
Link: https://github.com/Harvard-IACS/2021-CS109A/tree/master/content
13 684
How to Write a Great Data Science Resume
Writing a resume for data science job applications is rarely a fun task, but it is a necessary evil. The majority of companies require a resume in order to apply to any of their open jobs, and a resume is often the first layer of the process in getting past the “Gatekeeper” — the recruiter or hiring manager.
Link: https://www.dataquest.io/blog/how-data-science-resume-cv/
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Interesting SQL Resources You Must Read
1) 12 Best FREE SQL Courses and Certifications Online in 2022 [Bestseller]
Link: https://www.mltut.com/best-free-sql-courses/
2) How to Understand Long and Complex SQL Queries
Link: https://medium.com/codex/how-to-understand-long-and-complex-sql-queries-561dc87dff44
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 684
Importance of Theory in Data Science
While there are many resources covering the theoretical foundations of data science concepts, few demonstrate why having these foundations is practically important. This article gives four examples illustrating why it’s crucial for a data scientist to know what they’re doing
Link: https://towardsdatascience.com/the-importance-of-theory-in-data-science-3487b4e93953
13 684
How To Use Tableau and Python
TabPy (the Tableau Python Server) is an Analytics Extension implementation that expands Tableau’s capabilities by allowing users to execute Python scripts and saved functions via Tableau’s table calculations. You can learn more about it in this article
Link: https://medium.datadriveninvestor.com/introducing-tabpy-tableau-python-e812bf3f2632
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Silhouette coefficient: A score from -1 to 1 describing the clusters found during modeling. A score near zero indicates overlapping clusters, and scores less than zero indicate data points assigned to incorrect clusters. A
Stop words: A list of words removed by natural language processing tools when building your dataset. There is no single universal list of stop words used by all-natural language processing tools.
In supervised learning, every training sample from the dataset has a corresponding label or output value associated with it. As a result, the algorithm learns to predict labels or output values.
Test dataset: The data withheld from the model during training, which is used to test how well your model will generalize to new data.
Training dataset: The data on which the model will be trained. Most of your data will be here.
Transformer: A more modern replacement for RNN/LSTMs, the transformer architecture enables training over larger datasets involving sequences of data.
In unlabeled data, you don't need to provide the model with any kind of label or solution while the model is being trained.
In unsupervised learning, there are no labels for the training data. A machine learning algorithm tries to learn the underlying patterns or distributions that govern the data.
