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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
Mostrar más📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 813 suscriptores, ocupando la posición 2 416 en la categoría Educación y el puesto 5 038 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.
Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 70, y en las últimas 24 horas de 10, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.44% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 997 visualizaciones. En el primer día suele acumular 1 652 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 10 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
67 813
Suscriptores
+1024 horas
+127 días
+7030 días
Archivo de publicaciones
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
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✅ https://t.me/Codeprogrammer
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
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If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you.
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• Real interview-ready Q&A from top use cases
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• Common commands, schema creation, data filtering, null handling
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Best Data Science Archive Notes
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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! 💡
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1. Master the fundamentals of Statistics Understand probability, distributions, and hypothesis testing Differentiate between descriptive vs inferential statistics Learn various sampling techniques 2. Get hands-on with Python & SQL Work with data structures, pandas, numpy, and matplotlib Practice writing optimized SQL queries Master joins, filters, groupings, and window functions 3. Build real-world projects Construct end-to-end data pipelines Develop predictive models with machine learning Create business-focused dashboards 4. Practice case study interviews Learn to break down ambiguous business problems Ask clarifying questions to gather requirements Think aloud and structure your answers logically 5. Mock interviews with feedback Use platforms like Pramp or connect with peers Record and review your answers for improvement Gather feedback on your explanation and presence 6. Revise machine learning concepts Understand supervised vs unsupervised learning Grasp overfitting, underfitting, and bias-variance tradeoff Know how to evaluate models (precision, recall, F1-score, AUC, etc.) 7. Brush up on system design (if applicable) Learn how to design scalable data pipelines Compare real-time vs batch processing Familiarize with tools: Apache Spark, Kafka, Airflow 8. Strengthen storytelling with data Apply the STAR method in behavioral questions Simplify complex technical topics Emphasize business impact and insight-driven decisions 9. Customize your resume and portfolio Tailor your resume for each job role Include links to projects or GitHub profiles Match your skills to job descriptions 10. Stay consistent and track progress Set clear weekly goals Monitor covered topics and completed tasks Reflect regularly and adapt your plan as needed
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Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/
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🔹 Title:
IllumiCraft: Unified Geometry and Illumination Diffusion for
Controllable Video Generation
🔹 Publication Date:
Published on Jun 3
🔹 Abstract:
IllumiCraft integrates geometric cues in a diffusion framework to generate high-fidelity, temporally coherent videos from textual or image inputs. AI-generated summary Although diffusion-based models can generate high-quality and high-resolution video sequences from textual or image inputs, they lack explicit integration of geometric cues when controlling scene lighting and visual appearance across frames. To address this limitation, we propose IllumiCraft, an end-to-end diffusion framework accepting three complementary inputs: (1) high-dynamic-range (HDR) video maps for detailed lighting control; (2) synthetically relit frames with randomized illumination changes (optionally paired with a static background reference image) to provide appearance cues; and (3) 3D point tracks that capture precise 3D geometry information. By integrating the lighting, appearance, and geometry cues within a unified diffusion architecture, IllumiCraft generates temporally coherent videos aligned with user-defined prompts. It supports background-conditioned and text-conditioned video relighting and provides better fidelity than existing controllable video generation methods. Project Page: https://yuanze-lin.me/IllumiCraft_page
🔹 Links:
- arXiv Page: https://arxiv.org/abs/2506.03150
- PDF: https://arxiv.org/pdf/2506.03150
- Project Page: https://yuanze-lin.me/IllumiCraft_page/
- Github: https://github.com/yuanze-lin/IllumiCraft
🔹 Models citing this paper:
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🔹 Spaces citing this paper:
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Mathematics for Computer Science
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
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Repost from Github Top Repositories
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