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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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๐Ÿ“ˆ Analytical overview of Telegram channel Data Science & Machine Learning

Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 816 subscribers, ranking 2 113 in the Education category and 4 286 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 75 816 subscribers.

According to the latest data from 18 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 884 over the last 30 days and by 6 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.25%. Within the first 24 hours after publication, content typically collects 1.38% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 462 views. Within the first day, a publication typically gains 1 043 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_dataโ€

Thanks to the high frequency of updates (latest data received on 19 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

75 816
Subscribers
+624 hours
+1657 days
+88430 days
Posts Archive
3 Data Science Free courses by Microsoft๐Ÿ”ฅ๐Ÿ”ฅ 1. AI For Beginners - https://microsoft.github.io/AI-For-Beginners/ 2. ML For Beginners - https://microsoft.github.io/ML-For-Beginners/#/ 3. Data Science For Beginners - https://github.com/microsoft/Data-Science-For-Beginners Join for more: https://t.me/udacityfreecourse

Essential statistics topics for data science 1. Descriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data. 2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis. 3. Probability theory: Concepts of probability, random variables, and probability distributions. 4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling. 5. Statistical modeling: Linear regression, logistic regression, and time series analysis. 6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning. 7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods. 8. Data visualization: Techniques for visualizing data and communicating insights effectively. 9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results. 10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content ๐Ÿ˜„๐Ÿ‘

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Learning data science in 2024 will likely involve a combination of traditional educational methods and newer, more innovative approaches. Here are some steps you can take to learn data science in 2024: 1. Enroll in a data science program: Consider enrolling in a data science program at a university or online platform. Look for programs that cover topics such as machine learning, statistical analysis, and data visualization. I will recommend the subscription by 365datascience which update content as per latest requirements. 2. Take online courses: There are many online platforms that offer data science courses, such as Udacity, Udemy, and DataCamp. These courses can help you learn specific skills and techniques in data science. 3. Participate in data science competitions: Participating in data science competitions, such as those hosted on Kaggle, can help you apply your skills to real-world problems and learn from other data scientists. 4. Join data science communities: Joining data science communities, such as forums, meetups, or social media groups, can help you connect with other data scientists and learn from their experiences. 5. Stay updated on industry trends: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow blogs, podcasts, and industry publications to keep up with the latest developments in data science. 6. Build a portfolio: As you learn data science skills, be sure to build a portfolio of projects that showcase your abilities. This can help you demonstrate your skills to potential employers or clients. ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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Thanks for the amazing response. I added few more essential data science resources in "Projects" Folder today. ENJOY LEARNING
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Machine learning .pdf9.14 MB

๐Ÿ“ข Calling All Developers to talk with Founder from IIT Delhi to discover the future of Coding ๐Ÿ“ข Are you ready to supercharg
๐Ÿ“ข Calling All Developers to talk with Founder from IIT Delhi to discover the future of Coding ๐Ÿ“ข Are you ready to supercharge your productivity and take your coding skills to the next level? ๐Ÿš€ Event Details: Date : 9th May 2024 Time : 9:00 PM to 10:00 PM Register for free: https://www.buildfastwithai.com/events/10x-developer-productivity-with-ai Connect with Founder: https://www.linkedin.com/in/satvik-paramkusham/ This event is especially designed for people interested in Data Science, Data Analysis, GenAI and LLM.

Median and mode.pdf9.67 KB

Planning for Data engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. ๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐’๐๐‹ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. ๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

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Basics Of Statistics โœ”๏ธ.pdf2.30 MB

Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions. Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms. Join for more: t.me/datasciencefun

Overview of Machine Learning
Overview of Machine Learning

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Starting Out with Python Tony Gaddis, 2021

๐Ÿคฉ Quick Roadmaps to Learn ๐Ÿคฉ โค๏ธ Javascript https://roadmap.sh/javascript โค๏ธ Data Science https://miro.medium.com/max/828/1*UQ9M5X6R1LVPzwc4bfnt9w.webp โค๏ธ Frontend development https://i0.wp.com/css-tricks.com/wp-content/uploads/2018/07/modern-front-end-developer.png?ssl=1 โค๏ธ Data Analyst Roadmap https://t.me/sqlspecialist/379 โค๏ธ AI/ML https://i.am.ai/roadmap