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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

Ko'proq ko'rsatish

📈 Telegram kanali Data science/ML/AI analitikasi

Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 672 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 377-o'rinni va Hindiston mintaqasida 31 635-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 13 672 obunachiga ega bo‘ldi.

09 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 155 ga, so‘nggi 24 soatda esa 5 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 8.03% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.25% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 098 marta ko‘riladi; birinchi sutkada odatda 308 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...

Yuqori yangilanish chastotasi (oxirgi ma’lumot 10 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

13 672
Obunachilar
+524 soatlar
+197 kunlar
+15530 kunlar
Postlar arxiv
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DATA SCIENCE IN C PROGRAMMING LANGUAGE
DATA SCIENCE IN C PROGRAMMING LANGUAGE

MACHINE LEARNING
MACHINE LEARNING

9 ESSENTIAL MACHINE LEARNING ALGORITHMS
9 ESSENTIAL MACHINE LEARNING ALGORITHMS

ARRAYS
ARRAYS

MACHINE LEARNING
MACHINE LEARNING

MACHINE LEARNING
MACHINE LEARNING

WHATS AN ARRAY?
WHATS AN ARRAY?

DATA CLEANING STEPS
DATA CLEANING STEPS

PANDAS
PANDAS

DATA STRUCTURE
DATA STRUCTURE

A CHEAT SHEET FOR DATA STRUCTURE
A CHEAT SHEET FOR DATA STRUCTURE

KAFKA IN A NUTSHELL
KAFKA IN A NUTSHELL

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈: 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐏𝐫𝐨𝐦𝐩𝐭
𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈: 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐏𝐫𝐨𝐦𝐩𝐭

MACHINE LEARNING
MACHINE LEARNING

🚀 Fun Facts About Data Science 🚀 1️⃣ Data Science is Everywhere - From Netflix recommendations to fraud detection in banking, data science powers everyday decisions. 2️⃣ 80% of a Data Scientist's Job is Data Cleaning - The real magic happens before the analysis. Messy data = messy results! 3️⃣ Python is the Most Popular Language - Loved for its simplicity and versatility, Python is the go-to for data analysis, machine learning, and automation. 4️⃣ Data Visualization Tells a Story - A well-designed chart or dashboard can reveal insights faster than thousands of rows in a spreadsheet. 5️⃣ AI is Making Data Science More Powerful - Machine learning models are now helping businesses predict trends, automate processes, and improve decision-making. Stay curious and keep exploring the fascinating world of data science! 🌐📊 #DataScience #Python #AI #MachineLearning #DataVisualization

Data Science Projects to Land a 6 Figure Job
Data Science Projects to Land a 6 Figure Job

Data Science Techniques
Data Science Techniques

DATA SCIENCE CONCEPTS
DATA SCIENCE CONCEPTS

Mathematics for Data Science Roadmap Mathematics is the backbone of data science, machine learning, and AI. This roadmap covers essential topics in a structured way. --- 1. Prerequisites ✔ Basic Arithmetic (Addition, Multiplication, etc.) ✔ Order of Operations (BODMAS/PEMDAS) ✔ Basic Algebra (Equations, Inequalities) ✔ Logical Reasoning (AND, OR, XOR, etc.) --- 2. Linear Algebra (For ML & Deep Learning) 🔹 Vectors & Matrices (Dot Product, Transpose, Inverse) 🔹 Linear Transformations (Eigenvalues, Eigenvectors, Determinants) 🔹 Applications: PCA, SVD, Neural Networks 📌 Resources: "Linear Algebra Done Right" – Axler, 3Blue1Brown Videos --- 3. Probability & Statistics (For Data Analysis & ML) 🔹 Probability: Bayes’ Theorem, Distributions (Normal, Poisson) 🔹 Statistics: Mean, Variance, Hypothesis Testing, Regression 🔹 Applications: A/B Testing, Feature Selection 📌 Resources: "Think Stats" – Allen Downey, MIT OCW --- 4. Calculus (For Optimization & Deep Learning) 🔹 Differentiation: Chain Rule, Partial Derivatives 🔹 Integration: Definite & Indefinite Integrals 🔹 Vector Calculus: Gradients, Jacobian, Hessian 🔹 Applications: Gradient Descent, Backpropagation 📌 Resources: "Calculus" – James Stewart, Stanford ML Course --- 5. Discrete Mathematics (For Algorithms & Graphs) 🔹 Combinatorics: Permutations, Combinations 🔹 Graph Theory: Adjacency Matrices, Dijkstra’s Algorithm 🔹 Set Theory & Logic: Boolean Algebra, Induction 📌 Resources: "Discrete Mathematics and Its Applications" – Rosen --- 6. Optimization (For Model Training & Tuning) 🔹 Gradient Descent & Variants (SGD, Adam, RMSProp) 🔹 Convex Optimization 🔹 Lagrange Multipliers 📌 Resources: "Convex Optimization" – Stephen Boyd --- 7. Information Theory (For Feature Engineering & Model Compression) 🔹 Entropy & Information Gain (Decision Trees) 🔹 Kullback-Leibler Divergence (Distribution Comparison) 🔹 Shannon’s Theorem (Data Compression) 📌 Resources: "Elements of Information Theory" – Cover & Thomas --- 8. Advanced Topics (For AI & Reinforcement Learning) 🔹 Fourier Transforms (Signal Processing, NLP) 🔹 Markov Decision Processes (MDPs) (Reinforcement Learning) 🔹 Bayesian Statistics & Probabilistic Graphical Models 📌 Resources: "Pattern Recognition and Machine Learning" – Bishop --- Learning Path 🔰 Beginner: ✅ Focus on Probability, Statistics, and Linear Algebra ✅ Learn NumPy, Pandas, Matplotlib ⚡ Intermediate: ✅ Study Calculus & Optimization ✅ Apply concepts in ML (Scikit-learn, TensorFlow, PyTorch) 🚀 Advanced: ✅ Explore Discrete Math, Information Theory, and AI models ✅ Work on Deep Learning & Reinforcement Learning projects 💡 Tip: Solve problems on Kaggle, Leetcode, Project Euler and watch 3Blue1Brown, MIT OCW videos.