Data Science & Machine Learning
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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
إظهار المزيد
📈 نظرة تحليلية على قناة تيليجرام Data Science & Machine Learning
تُعد قناة Data Science & Machine Learning (@datasciencefun) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 77 621 مشتركاً، محتلاً المرتبة 2 017 في فئة التعليم والمرتبة 3 821 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 77 621 مشتركاً.
بحسب آخر البيانات بتاريخ 13 نوفمبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -142، وفي آخر 24 ساعة بمقدار -20، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 2.37%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.01% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 843 مشاهدة. وخلال اليوم الأول يجمع عادةً 781 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, accuracy, distribution, panda, dataset.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 14 نوفمبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
77 621
المشتركون
-2024 ساعات
-957 أيام
-14230 أيام
أرشيف المشاركات
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.
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