Data Science & Machine Learning
Відкрити в Telegram
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
Показати більше
📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 77 694 підписників, посідаючи 2 011 місце в категорії Освіта та 3 828 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 77 694 підписників.
За останніми даними від 10 листопада, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -93, а за останні 24 години на -5, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.28%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.99% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 549 переглядів. Протягом першої доби публікація в середньому набирає 767 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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”
Завдяки високій частоті оновлень (останні дані отримано 11 листопада, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
77 694
Підписники
-524 години
-377 днів
-9330 днів
Архів дописів
Deep Learning Crash Course for Beginners with Python
AI Publishing, 2021
"Approaching (Almost) Any Machine Learning Problem" book.
by 4x Kaggle grandmaster Abhishek Thakur
Industry Data Science vs Academia Data Science
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry 👇:
1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.
4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
A Handbook of Statistical Analyses Using Stata
Sophia Rabe-Hesketh, 2007
Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
👇👇
https://t.me/free4unow_backup/582
Master 20+ skills for just 99/Years
💰150+ courses to learn from
📺Access to Live workshops
📚Interview Preparation
💼Access to exclusive EdYoda JOB PORTAL
📜Download 150+ Course completion certificates
Start now: https://bit.ly/3OGOVE8
Offer valid for limited time only!
Building Feature Extraction with Machine Learning
Bharath H. Aithal, 2023
LetMeRead_net_PHP_and_MySQL_PHP_Programming_and_MySQL_For_Beginners.pdf1.57 MB
+1🔔+$2.000 Your account is credited with this amount!👆🏼These are the messages my subscribers get every day
🫂 My subscribers play according to my algorithm and get $2,000 or more at a time ⤵️
❇️ Join my team, I'll tell you how to get the algorithm right now!
▶️ Subscribe ✅ soon while my private channel is available! Last call!
https://t.me/+iK7c7hRPoeM1M2My
https://t.me/+iK7c7hRPoeM1M2My
PYTHON_DATA_SCIENCE_ESSENTIALS_THIRD_EDITION @computer_books.pdf6.63 MB
Long-term recruitment of promoters and long-term cooperation:
1: You need to promote our platform on Facebook, Twitter, Douyin, Telegram group, etc.
2: We calculate the advertising fee according to the number of people who recharge, and 1 recharge user will transfer you 2USDT promotion fee
3: Every time a user recharges, you can get a commission reward of 10% of his recharge amount
4: If you have a lot of real traffic, you can earn at least $5,000 per month on advertising fees + commissions
For cooperation, please click below to contact me by telegram
#ad
“I've never met someone who
Could even compare to you”
LANKS - Stronger Than • 2018
For more great songs, follow https://t.me/UncoveringMusicalTreasures
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
Getting Started with Streamlit for Data Science
Tyler Richards, 2021
Artificial Intelligence with Python Cookbook (Ben Auffarth).epub13.42 MB
Educational Data Science: Essentials, Approaches, and Tendencies
Alejandro Peña-Ayala, 2023
