Machine Learning with Python
Открыть в Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Больше
Сеть:📈 Аналитический обзор Telegram-канала Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 151 подписчиков, занимая 2 379 место в категории Образование и 4 752 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 151 подписчиков.
Согласно последним данным от 01 сентября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 84, а за последние 24 часа — 7, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.17%. В первые 24 часа после публикации контент обычно набирает 1.54% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 845 просмотров. В течение первых суток публикация набирает 1 052 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 02 сентября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
68 151
Подписчики
+724 часа
+247 дней
+8430 дней
Архив постов
PIA S5 Proxy solves all problems for AI developers.
🔥 Why top AI teams choose PIA S5 Proxy:
🔹 SOCKS5 proxy: as low as $0.045/IP
✅ Global high-quality IP | No traffic limit / static IP
✅ High success rate >99.9% | Ultra-low latency | Stable anti-ban
✅ Smart crawling API, support seamless integration
🔹 Unlimited traffic proxy: only $79/day
✅ Unlimited traffic | Unlimited concurrency | Bandwidth over 100Gbps | Customization supported
✅ Best for large-scale AI / LLM data collection
✅ Save up to 90% on crawling costs
✨ Exclusive for new users:
Enter the coupon code [AI Python] to enjoy a 10% discount!
🚀 Buy now: http://www.piaproxy.com/?co=piaproxy&ck=?ai
📕 Mastering NumPy – From Basics to Advanced
NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.
The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.
#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis🌟 Join the communities:
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Scientific Visualization: Python + Matplotlib
📚 Download: https://inria.hal.science/hal-03427242/document
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Best Statistics Notes ✅
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🎁❗️TODAY FREE❗️🎁
Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥
JOIN 👇
https://t.me/+VKT2Gy3kE6A4NTE5
https://t.me/+VKT2Gy3kE6A4NTE5
https://t.me/+VKT2Gy3kE6A4NTE5
Repost from Machine Learning
🟣 AI Paper by Hand ✍️
[1] 𝗪𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀? 𝗡𝗼𝘁 𝗔𝗹𝗹 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝗡𝗲𝗲𝗱𝗲𝗱
[2] 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝘁𝗿𝗶𝗻𝗴𝘀: 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗳𝗼𝗿 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
[3] 𝗠𝗢𝗗𝗘𝗟 𝗦𝗪𝗔𝗥𝗠𝗦: 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝘁𝗼 𝗔𝗱𝗮𝗽𝘁 𝗟𝗟𝗠 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝘃𝗶𝗮 𝗦𝘄𝗮𝗿𝗺 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲
[4] 𝗧𝗛𝗜𝗡𝗞𝗜𝗡𝗚 𝗟𝗟𝗠𝗦: 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
[5] 𝗢𝗽𝗲𝗻𝗩𝗟𝗔: 𝗔𝗻 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹
[6] 𝗥𝗧-𝟭: 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗔𝘁 𝗦𝗰𝗮𝗹𝗲
[7] π𝟬: 𝗔 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗔𝗰𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗥𝗼𝗯𝗼𝘁 𝗖𝗼𝗻𝘁𝗿𝗼𝗹
[8] 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻: 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗟𝗼𝗻𝗴-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗟𝗠 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘃𝗶𝗮 𝗩𝗲𝗰𝘁𝗼𝗿 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
[9] 𝗣-𝗥𝗔𝗚: 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗼𝗻 𝗘𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗘𝘃𝗲𝗿𝘆𝗱𝗮𝘆 𝗧𝗮𝘀𝗸
[10] 𝗥𝘂𝗔𝗚: 𝗟𝗲𝗮𝗿𝗻𝗲𝗱-𝗥𝘂𝗹𝗲-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀
[11] 𝗢𝗻 𝘁𝗵𝗲 𝗦𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗼𝗳 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗳𝗼𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
[12] 𝗠𝗶𝘅𝘁𝘂𝗿𝗲-𝗼𝗳-𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀: 𝗔 𝗦𝗽𝗮𝗿𝘀𝗲 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀
[13]-[14] 𝗘𝗱𝗶𝗳𝘆 𝟯𝗗: 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝟯𝗗 𝗔𝘀𝘀𝗲𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
[15] 𝗕𝘆𝘁𝗲 𝗟𝗮𝘁𝗲𝗻𝘁 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿: 𝗣𝗮𝘁𝗰𝗵𝗲𝘀 𝗦𝗰𝗮𝗹𝗲 𝗕𝗲𝘁𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗧𝗼𝗸𝗲𝗻𝘀
[16]-[18] 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗩𝟯 (𝗣𝗮𝗿𝘁 𝟭-𝟯)
[19] 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Ever noticed how differently designers and programmers react to "shared" ideas?
🎨 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬:
"Look, we have similar ideas!"
vs.
"No! You stole my idea!" 😭
💻 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐞𝐫𝐬:
"Man, I stole your code."
"It's not my code." 😎
🐼 "Comparison Between SQL and pandas" – A Handy Reference Guide
⚡️ As a data scientist, I often found myself switching back and forth between SQL and pandas during technical interviews. I was confident answering questions in SQL but sometimes struggled to translate the same logic into pandas – and vice versa.
🔸 To bridge this gap, I created a concise booklet in the form of a comparison table. It maps SQL queries directly to their equivalent pandas implementations, making it easy to understand and switch between both tools.
⚡ This reference guide has become an essential part of my interview prep. Before any interview, I quickly review it to ensure I’m ready to tackle data manipulation tasks using either SQL or pandas, depending on what’s required.
📕 Whether you're preparing for interviews or just want to solidify your understanding of both tools, this comparison guide is a great way to stay sharp and efficient.
#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
💊 𝗕𝗶𝗻𝗮𝗿𝘆 𝗢𝗽𝘁𝗶𝗼𝗻 𝗙𝗿𝗲𝗲 𝗦𝗶𝗴𝗻𝗮𝗹 💊
🔶Binary option Signal telegram channel
🔷 Join For Free Signal
🔶 5 Min Expire Signal
🔷 Free 10 - 15 Daily signals
🔶 Join us on Telegram Chsnnel
#ad
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
𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸
If you’re working with large datasets, tools like Pandas can hit limits fast. That’s where 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 comes in—designed to scale effortlessly across big data workloads.
𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝘆𝗦𝗽𝗮𝗿𝗸?
PySpark is the Python API for Apache Spark—a powerful engine for distributed data processing. It's widely used to build scalable ETL pipelines and handle millions of records efficiently.
𝗪𝗵𝘆 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗜𝘀 𝗮 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀:
✔️ Scales to handle massive datasets
✔️ Designed for distributed computing
✔️ Blends SQL with Python for flexible logic
✔️ Perfect for building end-to-end ETL pipelines
✔️ Supports integrations like Hive, Kafka, and Delta Lake
𝗤𝘂𝗶𝗰𝗸 𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.filter(df["age"] > 30).show()#PySpark #DataEngineering #BigData #ETL #ApacheSpark #DistributedComputing #PythonForData #DataPipelines #SparkSQL #ScalableAnalytics✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Are you enjoy our content? Advertise on this channel and reach a highly engaged audience and subscribers! 👉🏻
Adsince.io is a fast, easy, and 100% trustworthy website for promoting your products and services on Telegram channels. There are many Telegram channels on Adsince that are suitable for advertising, so come and visit us.
⚡️ Place your ad here in three simple steps:
1 Sign up
2 Top up the balance in a convenient way
3 Create your advertising post
✨ Start your promotion journey now!
👉 www.Adsince.io
👉 www.Adsince.io
Follow me on LinkedIn for more projects and jobs
https://www.linkedin.com/in/hussein-sheikho-4a8187246
Python Cheat Sheet
#AI #SentimentAnalysis #DataVisualization #pandas #Numpy #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience⚡️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly:
🙂 Positive sentiment
☹️ Negative sentiment
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
🔗 GitHub: https://lnkd.in/e_gk3hfe
📰 Article: https://lnkd.in/e_baNJd2
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
🔗 Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
9 machine learning concepts for ML engineers!
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1️⃣ 4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
2️⃣ 4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM
3️⃣ Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
4️⃣ Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
5️⃣ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
6️⃣ 15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
7️⃣ 11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
8️⃣ 11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
9️⃣ NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization🔗 Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Best Machine Learning Notes
#HuggingFace #FreeCourses #AI #MachineLearning #DeepLearning #LLM #Agents #python #PythonProgramming #ReinforcementLearning #AudioAI #ComputerVision #3DAI #DiffusionModels #OpenSourceAI Join to our WhatsApp 📱 channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
📀 55+ AI and Data Science Projects
💻 Often you read all these articles, watch online courses, but until you do a practical project, start coding, and implement the concepts in practice, you don't learn anything.
🔸 Here is a list of 55 projects in different categories:👇
1⃣ Large language models 🔸 Link
🔢 Fine-tuning LLMs 🔸 Link
🔢 Time series data analysis 🔸 Link
🔢 Computer Vision 🔸 Link
🔢 Data Science 🔸 Link
➖➖➖➖➖
⏪ You can also access all of the above projects through the following GitHub repo: 👇
┌ 📂 AI Data Guided Projects
└🐱 GitHub-Repos
Join to our WhatsApp 💬channel:
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🤗 HuggingFace is offering 9 AI courses for FREE!
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
1️⃣ LLM Course:
https://huggingface.co/learn/llm-course/chapter1/1
2️⃣ Agents Course:
https://huggingface.co/learn/agents-course/unit0/introduction
3️⃣ Deep Reinforcement Learning Course:
https://huggingface.co/learn/deep-rl-course/unit0/introduction
4️⃣ Open-Source AI Cookbook:
https://huggingface.co/learn/cookbook/index
5️⃣ Machine Learning for Games Course
https://huggingface.co/learn/ml-games-course/unit0/introduction
6️⃣ Hugging Face Audio course:
https://huggingface.co/learn/audio-course/chapter0/introduction
7️⃣ Vision Course:
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
8️⃣ Machine Learning for 3D Course:
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
9️⃣ Hugging Face Diffusion Models Course:
https://huggingface.co/learn/diffusion-course/unit0/1
#HuggingFace #FreeCourses #AI #MachineLearning #DeepLearning #LLM #Agents #ReinforcementLearning #AudioAI #ComputerVision #3DAI #DiffusionModels #OpenSourceAI Join to our WhatsApp 💬channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🎁 Your balance is credited $4,000 , the owner of the channel wants to contact you!
Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today
T.me/Lisainvestor
Be sure to take advantage of our gift, admission is free, don't miss the opportunity, change your life for the better.
You can follow the link :
https://t.me/+0DQSCADFTUA3N2Qx
