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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
Ko'proq ko'rsatish📈 Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 831 obunachidan iborat bo'lib, Taʼlim toifasida 2 106-o'rinni va Hindiston mintaqasida 4 234-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 75 831 obunachiga ega bo‘ldi.
21 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 770 ga, so‘nggi 24 soatda esa 8 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.15% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.09% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 385 marta ko‘riladi; birinchi sutkada odatda 827 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
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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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 22 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
75 831
Obunachilar
+824 soatlar
+717 kunlar
+77030 kunlar
Postlar arxiv
Kubeflow_for_Machine_Learning_From_Lab_to_Production_by_Trevor_Grant.pdf13.95 MB
Gant_Laborde_Learning_Tensorflow_js_Powerful_Machine_Learning_in.pdf6.71 MB
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Top free Data Science resources
@datasciencefun
1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html
2. Data Science Essentials
https://www.edx.org/course/data-science-essentials
3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse
4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI
5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY
6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM
7. CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/
8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf
9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx
10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE
Share the channel link with friends
http://t.me/datasciencefun
9 Best Machine Learning Use cases in our Daily Lives 🚀
👓 Youtube Recommendation
👓 Voice Assistants
👓 arrow Smartphone Camera
👓 Google Maps routes
👓 Email Filtering
👓 Search
👓 Translation
👓 Chatbots
👓 Fraud Protection
😉5 Machine Learning Algorithms with Project Ideas
📉Linear Regression -> House Price Prediction
📈Logistic Regression -> Loan Default Prediction
🗞️ SVM -> News Classification
🏛️ KNN -> Breast Cancer Classification
🧮 Naive Bayes -> Text Classification
Data Science Bookcamp Five real-world Python projects.pdf42.41 MB
Decision trees and Random forests?
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables.
Random Forest is a versatile machine learning method capable of performing both regression and classification tasks. It also undertakes dimensional reduction methods, treats missing values, outlier values and other essential steps of data exploration, and does a fairly good job. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.
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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
Pandas Tricks to Create a DataFrame From an Existing One.pdf5.32 KB
Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf11.81 MB
