Data Science & Machine Learning
前往频道在 Telegram
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
显示更多
📈 Telegram 频道 Data Science & Machine Learning 的分析概览
频道 Data Science & Machine Learning (@datasciencefun) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 77 621 名订阅者，在 教育 类别中位列第 2 017，并在 印度 地区排名第 3 821 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 77 621 名订阅者。
根据 13 十一月, 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 -142，过去 24 小时变化为 -20，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 2.37%。内容发布后 24 小时内通常能获得 1.01% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 843 次浏览，首日通常累积 781 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 learning, accuracy, distribution, panda, dataset 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：
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
凭借高频更新（最新数据采集于 14 十一月, 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 教育 类别中的关键影响点。
77 621
订阅者
-2024 小时
-957 天
-14230 天
帖子存档
LinkedIn - Python for Data Science Essential Training Part 2.zip390.08 MB
🌐 Join the researchers and programmers channel (Courses, Books, Papere and Codes).
t.me/DataScience_Books
Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications - Laura Igual, Santi Segui (Springer, 2017)
What is unsupervised learning?
Unsupervised learning aims to detect patterns in the data where no labels are given.
What are precision, recall, and F1-score?
Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).
Where TP is true positives, FP is false positives and FN is false negatives
In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.
F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.
How NASA Auto Colourise Images with Deep Learning?
Free Live Sessions on Aug 19th,20th @7.00pm IST
Register here :
https://bit.ly/2SLpFlw
Machine Learning God by Stefan Stavrev (version 2.0).pdf1.57 MB
Some useful Machine Learning projects for practice
https://elitedatascience.com/machine-learning-projects-for-beginners
https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f
https://www.springboard.com/blog/machine-learning-projects
https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional
Learn how Amazon Optimise Delivery Routes using Genetic Algorithm
👇👇
https://bit.ly/3izxvZJ
Free Live Session with Certificate
Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
🤓 Technical Python concepts tested in the data science job interviews are:
- Data types.
- Built-in data structures.
- User-defined data structures.
- Built-in functions.
- Loops and conditionals.
- External libraries (Pandas).
Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html
What are the benefits of a single decision tree compared to more complex models?
easy to implement
fast training
fast inference
good explainability
What are the decision trees?
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy.
What is feature selection? Why do we need it?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.
The session is going to go LIVE in 5 mins
So join in now !
Link - https://youtu.be/a8lGmSLcfWU
Do you want to Learn how to build a winning resume ?
Learn the basics from an expert in our mentor conference 2021 Resume session today!
Link to Join - https://youtu.be/a8lGmSLcfWU
If you are interested in getting a certificate and resources then kindly register at
www.foreignadmits.com/mentorconference2021
Note - Certificates will only be given to registered LIVE attendees
See you at the mentor conference
Join our community to get updates about other sessions -
https://t.me/foreignadmits_community
