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Machine Learning with Python

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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πŸ“ˆ 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 68 475 subscribers, ranking 2 406 in the Education category and 4 586 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 475 subscribers.

According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -74 over the last 30 days and by 12 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.07%. Within the first 24 hours after publication, content typically collects 1.55% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 789 views. Within the first day, a publication typically gains 1 062 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
  • Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œLearn 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 04 November, 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.

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68 475
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Convert CSV to JSON with Python ⭐️ Github http://t.me/codeprogrammer πŸ”’ πŸ”’ #deeplearning #AI #ML #python

[Coursera] Deep Learning Specialization What you’ll learn β€’ Build and train deep neural networks, identify key architecture p
[Coursera] Deep Learning Specialization What you’ll learn β€’ Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications β€’ Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow β€’ Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data β€’ Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering Specialization – 5 course series 1. Neural Networks and Deep Learning 2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 3. Structuring Machine Learning Projects 4. Convolutional Neural Networks 5. Sequence Models Available free in our Paid Channel https://t.me/+qvMSLM70zys1ZTJi

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βœ‚οΈ CSV Trimming CSV Trimming is a Python package designed to clean up crooked CSVs - the kind you get from parking sites, leg
βœ‚οΈ CSV Trimming CSV Trimming is a Python package designed to clean up crooked CSVs - the kind you get from parking sites, legacy systems, or poorly collected data - and transform them into clean, well-formatted CSVs with just one line of code. There is no need for complex settings or large language models. pip install csv_trimming
Python
import pandas as pd
from csv_trimming import CSVTrimmer

# Load your csv
csv = pd.read_csv("tests/documents/noisy/sicilia.csv")
# Instantiate the trimmer
trimmer = CSVTrimmer()
# And trim it
trimmed_csv = trimmer.trim(csv)
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