Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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Network:📈 Analytical overview of Telegram channel Machine Learning with Python
Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 160 subscribers, ranking 2 376 in the Education category and 4 740 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 160 subscribers.
According to the latest data from 05 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 51 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.53%. Within the first 24 hours after publication, content typically collects 1.57% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 728 views. Within the first day, a publication typically gains 1 071 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 insidead, learning, degree, evaluation, algorithm.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Thanks to the high frequency of updates (latest data received on 06 September, 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.
68 160
Subscribers
-124 hours
+577 days
+5130 days
Posts Archive
I'm sure you had an idea, but something got in the way and you didn't develop it. The channel "Usual thing" is about this, the author tries to implement different business ideas, but every day he encounters problems and discusses them with you.
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🟢 Yaoliang Yu, a professor at the School of Computer Science at the University of Waterloo, Canada, has published several free data science courses. These courses include machine learning, data science optimization, linear algebra and deep learning.
✅ The resources of each course include textbooks, assignments, articles and projects during the course.
🔖 Guide to free data science courses at the University of Waterloo:
┌ ➡️ CS794 Fall 2022
└ 🖥 Optimization for Data Science
┌ ➡️ CS480 Fall 2022
└ 🖥 Introduction to Machine Learning
┌ ➡️ CS794 Fall 2021
└ 🖥 Game Theoretic Methods in ML
┌ ➡️ CS480 Fall 2019
└ 🧠 Theory of Deep Learning
┌ ➡️ CS475 Spring 2018
└ 🖥 Computational Linear Algebra
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✈️ http://t.me/codeprogrammer ✅
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In 1989, Yann LeCun and his team trained a LeNet 1 CNN, which was able to detect handwriting.
They published a video showing how this model can read the numbers that were written manually on a piece of paper, and then the model gives the numbers electronically.
The Convolution Neural Network CNN algorithm is considered one of the algorithms that has influenced the world and we find it nowadays in many fields.
In general, everything that can be predicted from an image or video is a CNN.
Many researchers relied on this algorithm and derived many of the most famous models from it (ResNet, DenseNet, MobileNet, SqueezeNet, VGG)
There are many models that come under the name CNN
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🔺 The best GitHub repositories for learning Python
✅ Learn Python for Data Science in 2024
👨🏻💻 In the latest data science report 2024 , Python is still the top programming language for data science with 56.7% . Here I have put a list of the best Python repositories for data science , which will improve your coding skills and guide you on the path to data science mastery.💯
1️⃣ Learn Python 3 repo
🖥 A collection of Jupyter notebooks for learning Python.
🐱 GitHub repo link
2️⃣ The Algorithms repo
🖥 All algorithms implemented in Python for training.
🐱 GitHub repo link
3️⃣ Awesome Python repo
🖥 A list of great Python frameworks, libraries, software, and resources.
🐱 GitHub repo link
4️⃣ 100 Days of ML repo
🖥 Learning algorithms and building neural networks without any programming experience.
🐱 GitHub repo link
5️⃣ Cosmic Python book repo
🖥 A book on Python's functional architectural patterns for managing complexity.
🐱 GitHub repo link
6️⃣ A Byte of Python book repo
🖥 If you do not learn Python programming, start with this book.
🐱 GitHub repo link
7️⃣ Python Machine Learning book repo
🖥 Python Machine Learning book code repository.
🐱 GitHub repo link
8️⃣ Repo of interactive interview challenges
🖥 120+ interactive Python coding interview challenges.
🐱 GitHub repo link
9️⃣ Repo of coding problems
🖥 Solutions for various coding/algorithmic problems.
🐱 GitHub repo link
1️⃣ Python Basics repo
🖥 A list of 300 Python interview questions + answer sheet.
🐱 GitHub repo link
1️⃣ Python programming exercises repo
🖥 100+ challenging Python programming exercises.
🐱 GitHub repo link
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⚡️ Graph Machine Learning
Free advanced course: Machine learning on graphs .
The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore.
▪ Introduction
▪ Dive into graphs
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb
- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb
- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb
▪ Graph clustering
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb
- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb
- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb
- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb
https://pic.twitter.com/vSXCx364pe
▪ Lectures 4 Graph SVM
- Lab1 : Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb
- Lab2 : Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb
- Lab3 : Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb
- Lab4 : Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb
Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf
💡 Github
✅ https://t.me/DataScienceT
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The donor will receive a permanent subscription to our paid services
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Some Helpful Data Science Projects for Beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
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Repost from Machine Learning
Are you fluent in Python and want to evaluate your skills? 🤔
Do you want to learn Python? 🤗
Are you interested in learning through questions and answers? 😵
Do you want to receive the explanation of the question?💡
🟢 https://t.me/DataScienceQ 👍
✅ Learn PyTorch in 4 steps
👨🏻💻 With support for more than 200 different mathematical operations, PyTorch is one of the most powerful open source and computational libraries based on Python for machine learning. You don't need to look for different sources to learn this popular library, but the website of this library covers more than what you need.💯
2️⃣ Here, I will teach you how to learn PyTorch from scratch in 4 steps. 👌🏼
1️⃣ Text tutorial: If you don't know anything about Python, you should start learning from here.
⬅️ Topics: tensors, datasets and DataLoader, model building, optimization loop, saving, loading and using the model.
📄 PyTorch Basics
┘📎 Link: Learn the Basics
2️⃣ Video training: This section is very useful for those who prefer video content.
⬅️ Topics: introduction, tensor construction and model construction with pytorch.
📄 Introduction to PyTorch
┘📎 Link: Introduction to PyTorch
3️⃣ Examining examples: By viewing different examples, you will review what you have learned so far.
📄 Learning Python by example
┘📎 Link: Learning PyTorch with Examples
4️⃣ Coding: This step is vital! Your theoretical knowledge is worthless without coding! The Keras website is full of sample code that is great for getting started.
📄 Start coding
┘📎 Link: keras.io
Repost from N/a
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