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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
Показати більше📈 Аналітичний огляд Telegram-каналу Data science/ML/AI
Канал Data science/ML/AI (@datascience_bds) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 13 685 підписників, посідаючи 9 380 місце в категорії Технології та додатки та 31 607 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 13 685 підписників.
За останніми даними від 10 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 143, а за останні 24 години на 2, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 8.09%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.22% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 106 переглядів. Протягом першої доби публікація в середньому набирає 304 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як panda, learning, row, api, ethic.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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...”
Завдяки високій частоті оновлень (останні дані отримано 11 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
13 685
Підписники
+224 години
+217 днів
+14330 день
Архів дописів
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CS109 Data Science
By Harvard University
⌛️ 12 weeks
✅ Video lectures
✅ Slides
✅ Lab exercises
🔗 http://cs109.github.io/2015/pages/videos.html
Note: i have issues with first video link but others are fine.
#datascience #python #harvard
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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Source codes for data science projects from my Instagram post:
https://www.instagram.com/p/CJwDIpCA0nc/
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
#data_science #projects
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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Detailed Data Science Roadmap to become a Data Scientist
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Python course by kaggle
Learn the most important language for data science.
🎬 8 lessons
⏰ 5 hours
https://www.kaggle.com/learn/python
#python
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Join @bigdataspecialist for more
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Hey folks,
some of you probably already know that,
I have Instagram page where i share educational posts about data science and machine learning.
Your support in form of follow and possibly engagement on my posts would be very appreciated.
Instagram Page Link:
http://Instagram.com/bigdataspecialist
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 685
👩💻 5 FREE DATA SCIENCE COURSES FOR BEGINNERS 👩🏫
CS109 Data Science (Harvard) -
http://cs109.github.io/2015/pages/videos.html
Data-Driven Decision Making (PwC) -
https://www.coursera.org/learn/decision-making
Machine Learning (Stanford) -
https://www.coursera.org/learn/machine-learning
Data Science Foundations (IBM) -
https://cognitiveclass.ai/learn/data-science
Data Science Specialization (JHU) -
https://www.coursera.org/specializations/jhu-data-science
Subscribe for more helpful data science learning materials and free courses
#data_science
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 685
Best Statistic books for data science
Practical statistics for data scientists
by Peter Bruce and Andrew Bruce
🔗 Book Link
Think Stats
by Allen B. Downey
🔗 Book Link
Computer Age Statistical Inference
by Bradley Efron and Trevor Hastie
🔗 Book Link
Statistics in Plain English
by Timothy C. Urdan
🔗 Book Link
#Statistics #books #data_science
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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The "Approaching (Almost) Any Machine Learning Problem" book.
by 4x Kaggle grandmaster Abhishek Thakur
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Hey, of course, If i find nice graphical representation I will send you, but now i can tell you how did I use every of these algorithms at my work.
I used SVM for text and product classification (Some article belongs to sport category, some to business, medicine etc, similar with products, I used it to classify products into categories similar to what you have on Amazon.
I used KNN for simple classification problems, but generally we don't use it much in production as there are more advanced ones.
I used regression to predict continuous value as price of product.
I used random forest (and Gradient boosting algorithms like LightGBM and XGBOOST) for predicting possibility that person will convert on some ad (for example that person will buy a product advertised in an ad). Both Random Forest and Gradient Boosting are based on decision trees, they are very similar but gradient boosting is more advanced.
I used CNN for image recognition (finding patterns in images to recognize objects).
I haven't used RNN (Recurrent neural networks ) much but they are used for problems that are recursive by their nature. For example good usage of it in my work would be for some NLP tasks (sentences could be considered as recursive so its used on text and speech data). Also they are used to simulate neuron activity in our brain).
I used K-means for clusterization of articles or products into different unlabeled clusters. It helps to determine which articles/products are similar to each other.
I used PCA (Principal Component Analysis) to reduce number of dimensions for datasets that have too many of them. It also helped me to remove personal data from some datasets and model them as doubles (instead of names, surnames, date of birth etc).
I hope this helps. I will send this to main channel in case somebody else find it useful.
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The journey of Data Scientist
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⭐️ 15 Best Machine Learning Cheat Sheet ⭐️
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
#machine_learning #deep_learning #scikit-learn #keras
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*This channel belongs to @bigdataspecialist group
