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Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data
Показати більше📈 Аналітичний огляд Telegram-каналу DataSpoof
Канал DataSpoof (@dataspoof) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 16 129 підписників, посідаючи 12 548 місце в категорії Освіта та 26 541 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 16 129 підписників.
За останніми даними від 22 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -144, а за останні 24 години на -5, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 7.89%. Протягом перших 24 годин після публікації контент зазвичай збирає N/A% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 0 переглядів. Протягом першої доби публікація в середньому набирає 0 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 0.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як api, llm, pipeline, +9183182, engineer.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Data Science
https://dataspoof4081.graphy.com/membership
Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data”
Завдяки високій частоті оновлень (останні дані отримано 23 червня, 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.
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Підписники
-524 години
-277 днів
-14430 день
Архів дописів
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Best book on machine learning by Abhishek thakur
World first kaggle Grandmaster
Paperback version
https://amzn.to/3olDb9h
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Ask your questions related to Data science, machine learning, deep learning, computer vision and career related questions. You will get your answers within 24hrs
✋✋✋
https://t.me/joinchat/VgOmi4uB9OImdbLw
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130 Machine Learning Projects Solved and Explained
https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392
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The DataSpoof educational posts are going viral on Tumblr and Facebook as well.
http://dataspoof.tumblr.com
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https://www.instagram.com/p/CKac5Yihxtx/?igshid=13t66p3zki6k7
In the second part, we talk about
1- What are the different types of data
2- What is probability distribution
3- Types of Probability distribution
4- Definition of correlation and covariance
Like ❤ and share
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Harvard University has this Free course on Data Science : Machine Learning!
Link to enroll : http://bit.ly/2WtDPFZ
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Don't have enough data to train your model? Fret not! Use the synthetic one!
👉🏼 Synthetic data is artificially generated data that is not collected from real world events! It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.
🧠 Synthetic data can be used for many applications:
- Privacy
- Removing Bias
- Balancing Datasets
- Augment Datasets
👉🏼 Where to generate it from and how?
Open Source Project YData Synthetic: This repository contains material on GANs for synthetic data generation, especially regular tabular data and time-series. It consists a set of different GAN architectures developed using Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures.
Link: https://github.com/ydataai/ydata-synthetic
🌟 Star the repository to save it for future use or reference!
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Interview Question
When would you use standard Gradient Descent over Stochastic Gradient Descent, and vice-versa?
Standard Gradient Descent theoretically minimizes the error function better than Stochastic Gradient Descent. However, Stochastic Gradient Descent converges much faster once the dataset becomes large. Thus standard Gradient Descent is preferable for small datasets while Stochastic Gradient Descent is preferable for the larger ones.
In practice, however, Stochastic Gradient Descent is used for most of the applications because it minimizes the error function well enough while being much faster and more memory efficient for large datasets.
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Interview Question
What is the difference between Data Mining and Machine Learning?
Machine Learning is a branch of Artificial Intelligence which aims at making systems learn automatically from the data provided and improve their learning over time without being explicitly programmed. Data Mining, on the other hand, focuses on analyzing the data and extracting knowledge and/or unknown interesting patterns from it. The goal is to understand the patterns in the data in order to explain some phenomenon and not to develop a sophisticated model which can predict the outcomes for the unknown/new data. For instance, you can use Data Mining on the existing data to understand your company’s sales trends and then build a Machine Learning Model to learn from that data, find the correlations and adapt for the new data.
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Interview Question
Define Sampling. Why do we need it?
Sampling is a process of choosing a subset from a target population which would
serve as its representative. We use the data from the sample to understand the
pattern in the population as a whole. Sampling is necessary because often we can
not gather or process the complete data within a reasonable time. There are many
ways to perform sampling. Some of the most commonly used techniques are
Random Sampling, Stratified Sampling, and Clustering Sampling.
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Interview Question
Q. How is k-Nearest Neighbors (k-NN) different from k-Means algorithm?
A. The fundamental difference between these algorithms is that k-NN
is a Supervised algorithm whereas k-means is Unsupervised in nature.
B. k-NN is a Classification (or Regression) algorithm and k-means is a
Clustering algorithm.
C. k-NN tries to classify an observation based on its "k" surrounding
neighbors. It is also known as a lazy learner because it does absolutely
nothing at the training stage. On the other hand, k-means algorithm partitions
the training data set into different clusters such that all the data points in a
cluster are closer to each other than the data points from other clusters. The
algorithm tries to maintain enough separability between these clusters.
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Why to study machine learning
Why Study Algorithms?
Computer scientists learn by experience. We learn by seeing others solve problems and by solving problems by ourselves. Being exposed to different problem-solving techniques and seeing how different algorithms are designed helps us to take on the next challenging problem that we are given. By considering a number of different algorithms, we can begin to develop pattern recognition so that the next time a similar problem arises, we are better able to solve it.
Algorithms are often quite different from one another. Consider the example of sqrt seen earlier. It is entirely possible that there are many different ways to implement the details to compute the square root function. One algorithm may use many fewer resources than another. One algorithm might take 10 times as long to return the result as the other. We would like to have some way to compare these two solutions. Even though they both work, one is perhaps “better” than the other. We might suggest that one is more efficient or that one simply works faster or uses less memory. As we study algorithms, we can learn analysis techniques that allow us to compare and contrast solutions based solely on their own characteristics, not the characteristics of the program or computer used to implement them.
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There is a telegram group where you can get job related post related to Data science, analyst, machine learning engineer.
Hope you will like it
https://t.me/jobseekersarena
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There is no direct way to become data scientist. You have to be some experience first
