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

Network:Machine Learning with Python India4 731 Education2 365...

📈 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 68 138 subscribers, ranking 2 365 in the Education category and 4 731 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 138 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 80 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 4.09%. Within the first 24 hours after publication, content typically collects 1.54% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 784 views. Within the first day, a publication typically gains 1 052 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 01 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.

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68 138

Subscribers

+124 hours

-97 days

+8030 days

2 784

Post views

~ 1 05224 hours

~ 1 13048 hours

4.09%

Engagement rate

~ 2

Posts per day

Ads index

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Posts Archive

68 138

Where are you from? Put the flag of your country

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Machine learning and deep learning ✅@Machine_learn Large language Model Git 🔺https://t.me/deep_learning_proj

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This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well. Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too. Read it online: https://visionbook.mit.edu/

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Top 50 LLM Interview Questions!

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Top 50 LLM Interview Questions! A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations. 👇👇👇👇👇👇

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New to Pandas? Here's a cheat sheet you can download (2025)

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The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners. Enroll Free: https://introtodeeplearning.com/

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Repost from Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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ask me https://t.me/m/-nTmpj5vYzNk

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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub: 1️⃣ Text-Based Applications 1.1. Building a Chatbot Using HuggingFace Open Source Models https://lnkd.in/dku3bigK 1.2. Building a Text Translation System using Meta NLLB Open-Source Model https://lnkd.in/dgdjaFds 2️⃣ Speech-Based Applications 2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model https://lnkd.in/dbgQgDyn 2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio https://lnkd.in/dcbp-8fN 2.3. Building Text-to-Speech Systems Using VITS & ArTST Models https://lnkd.in/dwFcQ_X5 3️⃣ Image-Based Applications 3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model https://lnkd.in/dnk6epGB 3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide https://lnkd.in/d573SvYV 3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM) https://lnkd.in/dFavEdHS 3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio https://lnkd.in/d9jjJu_g 4️⃣ Vision Language Applications 4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models https://lnkd.in/dHNFaHFV 4.2. Building an Image Captioning System using Salesforce Blip Model https://lnkd.in/dh36iDn9 4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models https://lnkd.in/d7fsJEAF ➡️ You can find the articles and the codes for each article in this GitHub repo: https://lnkd.in/dG5jfBwE

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🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph Link: https://realpython.com/langgraph-python/

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 – 𝗔𝗹𝗹-𝗶𝗻-𝗢𝗻𝗲 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you. I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce • Spark Architecture – Driver, Executors, DAG • RDDs vs DataFrames vs Datasets • SparkContext vs SparkSession • Transformations: map, flatMap, reduceByKey, groupByKey • Optimizations – caching, persisting, skew handling, salting • Joins – Broadcast joins, Shuffle joins • Deployment modes – Cluster vs Client • Real interview-ready Q&A from top use cases • CSV, JSON, Parquet, ORC – Format comparisons • Common commands, schema creation, data filtering, null handling 𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.

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Best Data Science Archive Notes

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68 138

AI vs ML vs Deep Learning vs Generative AI

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Repost from Remote Jobs — Daily LinkedIn Picks 💼

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🔍 Understanding Recurrent Neural Networks (RNNs) Cheat Sheet! Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started: 📘 Key Concepts: Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters. Hidden State: Maintains information from previous inputs, enabling memory across time steps. Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time. 🔧 Common Variants: Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow. Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture. 🚀 Applications: Language Modeling: Predicting the next word in a sentence. Sentiment Analysis: Understanding sentiments in text. Time-Series Forecasting: Predicting future data points in a series. 🔗 Resources: Dive deeper with tutorials on platforms like Coursera, edX, or YouTube. Explore open-source libraries like TensorFlow or PyTorch for implementation. Let's harness the power of RNNs to innovate and solve complex problems! 💡