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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 75 837 obunachidan iborat bo'lib, Taʼlim toifasida 2 107-o'rinni va Hindiston mintaqasida 4 219-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 75 837 obunachiga ega bo‘ldi.
22 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 728 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.00% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.05% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 278 marta ko‘riladi; birinchi sutkada odatda 794 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 23 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.
75 837
Obunachilar
-224 soatlar
+637 kunlar
+72830 kunlar
Postlar arxiv
👉A handy notebook on handling missing values
Link : 👇👇
https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python
A list of NLP Tutorials
Link : 👇👇
https://github.com/lyeoni/nlp-tutorial
“An Implementation and Explanation of the Random Forest in Python” by Will Koehrsen 👇👇
https://link.medium.com/GCWFv81v95
“How to analyse 100s of GBs of data on your laptop with Python” by Jovan Veljanoski 👇👇
https://link.medium.com/V8xS82Cax6
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Recall how many of the true positives were recalled (found), i.e. how many of the correct hits were also found. Its formula would be
Precision is one indicator of a machine learning model's performance – the quality of a positive prediction made by the model. Its formula would be?
Seeing Theory : A visual introduction to probability and statistics
Link :👇👇
https://seeing-theory.brown.edu/
“The Projects You Should Do to Get a Data Science Job” by Ken Jee
👇👇
https://link.medium.com/Q2DnxSGRO6
👉The Ultimate Guide to the Pandas Library for Data Science in Python
👇👇
https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/
A Visual Intro to NumPy and Data Representation
.
Link : 👇👇
https://jalammar.github.io/visual-numpy/
Matplotlib Cheatsheet 👇👇
https://github.com/rougier/matplotlib-cheatsheet
SQL Cheatsheet 👇👇
https://websitesetup.org/sql-cheat-sheet/
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Start working on any project if you are a beginner and want to grow your career as a data scientist
You will learn much more as you practice and work on projects from yourself
You can find dataset in this channel or go to kaggle to find any random dataset and just work on it
Learning concepts is fine but most of the learnings come from projects
I know that might feel boring at first time but as you move forward, it become interesting
K-means vs DBScan ML Algorithm
DBScan is more robust to noise.
DBScan is better when the amount of clusters is difficult to guess.
K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.
What is the curse of dimensionality? Why do we care about it?
Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.
Dimensionality reduction techniques
Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
T-distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Fourier and Wavelet Transforms
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Chatbot project using ML
Before using this you have to install
Tensorflow, keras , pickle, nltk by using pip install in command prompt
Fake news Detection Machine Learning Project with 92%Accuracy
it contain compressed file in which "jupyter notebook file and dataset"✅
