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Data Science & Machine Learning

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

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📈 Telegram kanali Data Science & Machine Learning analitikasi

Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 822 obunachidan iborat bo'lib, Taʼlim toifasida 2 109-o'rinni va Hindiston mintaqasida 4 254-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 75 822 obunachiga ega bo‘ldi.

20 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 833 ga, so‘nggi 24 soatda esa 1 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 1.15% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 2 391 marta ko‘riladi; birinchi sutkada odatda 875 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

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
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 21 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.

75 822
Obunachilar
+124 soatlar
+1047 kunlar
+83330 kunlar
Postlar arxiv
+2
Deep Learning Applications 2 M. Arif Wani, 2021

The Data Science Handbook Field Cady, 2017

Data Science Interview Questions and Answers 👨‍💻.pdf13.81 MB

The Data Science Handbook Carl Shan, 2015

ML_Projects_270.pdf3.69 KB

devops-1.pdf1.91 MB

Pandas loc & iloc Function.pdf0.50 KB

SparkNotes.pdf2.30 KB

+1
Foundational Python for Data Science.pdf26.26 MB

🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at o
🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/Dxfog, make your choice and apply now while there are still seats available. See you there! ▶️ December 12 - Most In-Demand IT Jobs 2023: Become a Systems Engineer ▶️ December 13 - Tech Jobs for Beginners: Become a Software Tester ▶️ December 15 - Most In-Demand IT Jobs 2023: Become a Software Tester ▶️ January 5 - UX Design. First Free Lesson ▶️ January 9 - Sales Engineering. First Free Lesson Special offer for all participants! ️ ✅ Apply by the link https://crst.co/Dxfog 

An high level overview for becoming a machine learning engineer
An high level overview for becoming a machine learning engineer

Practical MLops.pdf1.69 MB

DATA CLEANING AND PROCESSING.pdf2.26 MB

Stats Notes 1.pdf4.06 MB

Cheatsheet Supervised Learning.pdf6.41 KB

What topic does AI cover
What topic does AI cover

Data Science Bookcamp Leonard Apeltsin, 2021

Deep Learning from Scratch Seth Weidman, 2019

1. What do you understand by the term silhouette coefficient? The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score. 2. What is the difference between trend and seasonality in time series? Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again. 3. What is Bag of Words in NLP? Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order. 4. What is the difference between bagging and boosting? Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm ENJOY LEARNING 👍👍

Hands on Plotly👍.pdf7.53 KB