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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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Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertTarmoq:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertHindiston4 234Taʼlim2 106

📈 Telegram kanali Data Science & Machine Learning analitikasi

Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 833 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, 75 833 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

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 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.

75 833
Obunachilar
+824 soatlar
+717 kunlar
+77030 kunlar
Postlar arxiv
Data Science & Machine Learning
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The Pandas Workshop.pdf28.94 MB

Data Science & Machine Learning
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Introduction to Machine Learning with Applications in Information Security Mark Stamp, 2022

Data Science & Machine Learning
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Machine Learning Projects 👇👇 https://t.me/Programming_experts/133

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ML Cheatsheet.pdf1.25 MB

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Azure Data Scientist Associate Certification Guide Andreas Botsikas, 2021

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Super VIP cheat sheet for Data Scientists.pdf7.12 MB

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Statistical Mechanics of Neural Networks Haiping Huang, 2021

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Data Analyst Interview Questions [Python, SQL, PowerBI] 1. Is indentation required in python? Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well. 2. What are Entities and Relationships? Ans: Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities. Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database. 3. What are Aggregate and Scalar functions? Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value. 4. What are Custom Visuals in Power BI? Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI ENJOY LEARNING 👍👍

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Efficient Methods for DL.pdf9.72 MB

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Useful Pandas🐼 method you should definitely know ✅ head() ✅ info() ✅ fillna() ✅ melt() ✅ pivot() ✅ query() ✅ merge() ✅ assign() ✅ groupby() ✅ describe() ✅ sample() ✅ replace() ✅ rename()

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To become a Machine Learning Engineer: • Python • numpy, pandas, matplotlib, Scikit-Learn • TensorFlow or PyTorch • Jupyter, Colab • Analysis > Code • 99%: Foundational algorithms • 1%: Other algorithms • Solve problems ← This is key • Teaching = 2 × Learning • Have fun!

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BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

Data Science & Machine Learning
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BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

Data Science & Machine Learning
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Natural Language Processing with TensorFlow Thushan Ganegedara, 2022

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BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

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+1
Linear Algebra and learning from data Gilbert Strang, 2019

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BTP CRYPTO PUMPS & SIGNALS #byAdsly
BTP CRYPTO PUMPS & SIGNALS #byAdsly

Data Science & Machine Learning
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Basics of Linear Algebra for Machine Learning Jason Brownlee, 2018

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Practical Linear Algebra For Data Science Mike X. Cohen, 2022

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Think Stats Allen B. Downey, 2011