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Data science/ML/AI

Data science/ML/AI

前往频道在 Telegram

Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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📈 Telegram 频道 Data science/ML/AI 的分析概览

频道 Data science/ML/AI (@datascience_bds) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 13 905 名订阅者,在 技术与应用 类别中位列第 8 911,并在 印度 地区排名第 28 819

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 13 905 名订阅者。

根据 30 八月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 90,过去 24 小时变化为 7,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 7.35%。内容发布后 24 小时内通常能获得 2.05% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 022 次浏览,首日通常累积 285 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 5
  • 主题关注点: 内容集中在 panda, learning, row, api, ethic 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...

凭借高频更新(最新数据采集于 31 八月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。

13 905
订阅者
+724 小时
+17
+9030
帖子存档
NVIDIA GTC is the most important conference for the era of AI and the metaverse. Join us online as we explore the innovations
NVIDIA GTC is the most important conference for the era of AI and the metaverse. Join us online as we explore the innovations that will impact your life’s work with the power of AI, computer graphics, data science, and more. The conference will run from September 19 -22, it is fully virtual and free to attend. Let innovation inspire your next idea, or solve your biggest challenge. With talks delivered by pioneers in their fields to relatable use cases and Deep Learning Institue training where you can gain NVIDIA certification, to Watch Parties where you can engage with your peers, you can be part of what comes next at GTC. Register today.

THE LAND OF CONFUSION!!!😱😱 If you have ever tried to tackle a classification problem you must have considered confusion mat
THE LAND OF CONFUSION!!!😱😱 If you have ever tried to tackle a classification problem you must have considered confusion matrices as a metric for evaluating your model performance. Confusion matrices🥲 can be quite confusing when encountered for the first time, But here's a trick. 1) Consider your target variable as the positive value in your matrix, while the other is negative. 2) Working with our photo above, we are more concerned about default, if default was correctly predicted is a True Positive 3)If default was incorrectly predicted, it's a False Positive 5) If not default was predicted to be not default correctly, it's a True Negative 6) If not default was incorrectly predicted as default, it's a False Negative

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Implementing DBSCAN in Python DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based unsupervised learning algorithm. It computes nearest neighbor graphs to find arbitrary-shaped clusters and outliers. Whereas the K-means clustering generates spherical-shaped clusters. Learn more about working with it in this article Link

Hello Dear😊!!! Have you heard of The Python For Machine Learning International Bootcamp coming up on the 12th of September?
Hello Dear😊!!! Have you heard of The Python For Machine Learning International Bootcamp coming up on the 12th of September? Link: Click Me If you haven't, Global AI Hub is organizing a FREE ONE-MONTH INTENSIVE boot camp on python for machine learning. This is a chance to improve yourselves in subjects such as Python😍, #machinelearning😍, #datascience😍, and #deeplearning😍!!! In addition, you will be able to develop your portfolios ☺️ with the project work😃 that you will do from scratch under the guidance of mentors!!!😁 Does this look very interesting to you, click the link in this post to register Link: Click Me DEADLINE😱😱 : 7th September 2022

Efficient Python Tricks and Tools for Data Scientists - By Khuyen Tra GithubRepo : https://github.com/khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists Stars ⭐️: 675 Forked By: 202

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ARTIFICIAL INTELLIGENCE FOR BEGINNERS Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum
ARTIFICIAL INTELLIGENCE FOR BEGINNERS Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence. In this curriculum, you will learn: ⭐️Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI). ⭐️Neural Networks and Deep Learning, which are at the core of modern AI. It illustrates the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch. ⭐️Neural Architectures for working with images and text. It covers recent models but may lack a little bit on the state-of-the-art. ⭐️Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems. Course Link #ai #ml #neural_networks #machine_learning #data_science #deep_learning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Types of Regression Analysis in Machine Learning If you are looking to dive deeper into Regression Analysis for Machine Learning and understand how to choose the right type of regression analysis model for your project, here's an article that can help. Link: https://www.projectpro.io/article/types-of-regression-analysis-in-machine-learning/410

👉Here's an amazing self explanatory infographics that depicts the SQL Join clause with each category quite easily. 📍Types o
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A WELL CONCISED INTRODUCTION TO REINFORCEMENT LEARNING Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. This article will guide you through understanding RL and it's applications. Link: Read Me👀 What you will learn: 👌How RL Works 👌Examples of RL 👌Benefits of RL 👌Challenges of RL 👌Future of RL

Harvard University Data Science Course 2021 Link: https://github.com/Harvard-IACS/2021-CS109A/tree/master/content
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How to Write a Great Data Science Resume Writing a resume for data science job applications is rarely a fun task, but it is a necessary evil. The majority of companies require a resume in order to apply to any of their open jobs, and a resume is often the first layer of the process in getting past the “Gatekeeper” — the recruiter or hiring manager. Link: https://www.dataquest.io/blog/how-data-science-resume-cv/

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Data science/ML/AI - Telegram 频道 @datascience_bds 的统计与分析