Data Science & Machine Learning

Open in 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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India4 334 Education2 114...

📈 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 75 795 subscribers, ranking 2 114 in the Education category and 4 334 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 795 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 936 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 3.44%. Within the first 24 hours after publication, content typically collects 1.39% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 606 views. Within the first day, a publication typically gains 1 052 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 16 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.

75 795

Subscribers

+624 hours

+2237 days

+93630 days

2 606

Post views

~ 1 05224 hours

~ 1 41248 hours

3.44%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

75 802

Important Python Functions 👆

75 802

𝟲 𝗙𝗥𝗘𝗘 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿!😍 Want to break into Data Analytics but don’t know where to start? These 6 FREE courses cover everything—from Excel, SQL, Python, and Power BI to Business Math & Statistics and Portfolio Projects! 📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4kMSztw 📌 Save this now and start learning today!

75 802

If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order): 1. SQL 2. Python 3. ML fundamentals 4. DSA 5. Testing 6. Prob, stats, lin. alg 7. Problem solving And building as much as possible.

75 802

Python Cheatsheet

75 802

Common Machine Learning Algorithms! 1️⃣ Linear Regression ->Used for predicting continuous values. ->Models the relationship between dependent and independent variables by fitting a linear equation. 2️⃣ Logistic Regression ->Ideal for binary classification problems. ->Estimates the probability that an instance belongs to a particular class. 3️⃣ Decision Trees ->Splits data into subsets based on the value of input features. ->Easy to visualize and interpret but can be prone to overfitting. 4️⃣ Random Forest ->An ensemble method using multiple decision trees. ->Reduces overfitting and improves accuracy by averaging multiple trees. 5️⃣ Support Vector Machines (SVM) ->Finds the hyperplane that best separates different classes. ->Effective in high-dimensional spaces and for classification tasks. 6️⃣ k-Nearest Neighbors (k-NN) ->Classifies data based on the majority class among the k-nearest neighbors. ->Simple and intuitive but can be computationally intensive. 7️⃣ K-Means Clustering ->Partitions data into k clusters based on feature similarity. ->Useful for market segmentation, image compression, and more. 8️⃣ Naive Bayes ->Based on Bayes' theorem with an assumption of independence among predictors. ->Particularly useful for text classification and spam filtering. 9️⃣ Neural Networks ->Mimic the human brain to identify patterns in data. ->Power deep learning applications, from image recognition to natural language processing. 🔟 Gradient Boosting Machines (GBM) ->Combines weak learners to create a strong predictive model. ->Used in various applications like ranking, classification, and regression. Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y ENJOY LEARNING 👍👍

75 802

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗘𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀𝗹𝘆 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁!🔥 Struggling with SQL basics?👋 This cheat sheet has everything you need! 🎯 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4hB8KYa 🚀 No more searching for syntax—just bookmark and use it anytime!

75 802

Python Topics with Projects ✅

75 802

🚀 BITCOIN OVER 75.000$ ! In the last 3 days my subscribers have made over $20.000$ with my help ! We are now showing how to make money trading with Lisa for free in our channel! Our subscriber on average earns 3.000$ on full passive just by trading with Lisa. We have now opened free access to our VIP channel, only 100 people will get in for free, time is limited! JOIN THE CHANNEL AND TRADE WITH LISA https://t.me/+GYtd75CySc05YTYy https://t.me/+GYtd75CySc05YTYy https://t.me/+GYtd75CySc05YTYy

75 802

A-Z of Data Science Part-2

75 802

A-Z of Data Science Part-1

75 802

To be GOOD in Data Science you need to learn: - Python - SQL - PowerBI To be GREAT in Data Science you need to add: - Business Understanding - Knowledge of Cloud - Many-many projects But to LAND a job in Data Science you need to prove you can: - Learn new things - Communicate clearly - Solve problems There is no way around. Follow this guide and get your first job in Data 🦾

75 802

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗘𝘅𝗰𝗲𝗹 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟳 𝗱𝗮𝘆𝘀? 📊 Here's a structured roadmap to help you go from beginner to pro in a week! Whether you're learning formulas, functions, or data visualization, this guide covers everything step by step. 𝐋𝐢𝐧𝐤👇 :- https://pdlink.in/43lzybE All The Best 💥

75 802

Ai concepts explained

75 802

Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://t.me/datalemur Like if you need similar content 😄👍

75 802

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊🚀 Want to master data analytics? Here are top free courses, books, and certifications to help you get started with Power BI, Tableau, Python, and Excel. 𝐋𝐢𝐧𝐤👇 https://pdlink.in/41Fx3PW All The Best 💥

75 802

Advanced AI and Data Science Interview Questions 1. Explain the concept of Generative Adversarial Networks (GANs). How do they work, and what are some of their applications? 2. What is the Curse of Dimensionality? How does it affect machine learning models, and what techniques can be used to mitigate its impact? 3. Describe the process of hyperparameter tuning in deep learning. What are some strategies you can use to optimize hyperparameters? 4. How does a Transformer architecture differ from traditional RNNs and LSTMs? Why has it become so popular in natural language processing (NLP)? 5. What is the difference between L1 and L2 regularization, and in what scenarios would you prefer one over the other? 6. Explain the concept of transfer learning. How can pre-trained models be used in a new but related task? 7. Discuss the importance of explainability in AI models. How do methods like LIME or SHAP contribute to model interpretability? 8. What are the differences between Reinforcement Learning (RL) and Supervised Learning? Can you provide an example where RL would be more appropriate? 9. How do you handle imbalanced datasets in a classification problem? Discuss techniques like SMOTE, ADASYN, or cost-sensitive learning. 10. What is Bayesian Optimization, and how does it compare to grid search or random search for hyperparameter tuning? 11. Describe the steps involved in developing a recommendation system. What algorithms might you use, and how would you evaluate its performance? 12. Can you explain the concept of autoencoders? How are they used for tasks such as dimensionality reduction or anomaly detection? 13. What are adversarial examples in the context of machine learning models? How can they be used to fool models, and what can be done to defend against them? 14. Discuss the role of attention mechanisms in neural networks. How have they improved performance in tasks like machine translation? 15. What is a variational autoencoder (VAE)? How does it differ from a standard autoencoder, and what are its benefits in generating new data? I have curated the best interview resources to crack Data Science Interviews 👇👇 https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D Like if you need similar content 😄👍

75 802

Python Detailed Roadmap 🚀 📌 1. Basics ◼ Data Types & Variables ◼ Operators & Expressions ◼ Control Flow (if, loops) 📌 2. Functions & Modules ◼ Defining Functions ◼ Lambda Functions ◼ Importing & Creating Modules 📌 3. File Handling ◼ Reading & Writing Files ◼ Working with CSV & JSON 📌 4. Object-Oriented Programming (OOP) ◼ Classes & Objects ◼ Inheritance & Polymorphism ◼ Encapsulation 📌 5. Exception Handling ◼ Try-Except Blocks ◼ Custom Exceptions 📌 6. Advanced Python Concepts ◼ List & Dictionary Comprehensions ◼ Generators & Iterators ◼ Decorators 📌 7. Essential Libraries ◼ NumPy (Arrays & Computations) ◼ Pandas (Data Analysis) ◼ Matplotlib & Seaborn (Visualization) 📌 8. Web Development & APIs ◼ Web Scraping (BeautifulSoup, Scrapy) ◼ API Integration (Requests) ◼ Flask & Django (Backend Development) 📌 9. Automation & Scripting ◼ Automating Tasks with Python ◼ Working with Selenium & PyAutoGUI 📌 10. Data Science & Machine Learning ◼ Data Cleaning & Preprocessing ◼ Scikit-Learn (ML Algorithms) ◼ TensorFlow & PyTorch (Deep Learning) 📌 11. Projects ◼ Build Real-World Applications ◼ Showcase on GitHub 📌 12. ✅ Apply for Jobs ◼ Strengthen Resume & Portfolio ◼ Prepare for Technical Interviews Like for more ❤️💪

75 802

Repost from Star Union News

When will the green summons end? In Germany, the green turn began in the noughties. This means that now every fifth windmill in the country has been operating for 20-25 years. That is, they are about to work out their standard service life and are likely to be demolished. Horror for the real economy. Old windmills will be replaced with new ones. And these are new subsidies and another increase in electricity prices." However, the number of generators will remain the same. This cycle will now be endless: we demolish the old, build the new (this is the motivation to support the "green" so actively).

"The energy transition has given the elites a clear conscience and at the same time a good profit margin,"

says Michael Vassiliadis, head of the Mining, Chemical and Energy Industrial Union(IG BCE). 🔥According to a Welt investigation in 2021, the environmental impact of the agenda brings a lot of profit to individuals. Representatives of environmental NGOs work closely with the Federal Government. How will this affect the industry? Automotive industry. The auto industry has lost 11,000 jobs over the past year. The outlook for the steel and electrical industries is daunting: Gesamtmetall, a lobbying group, predicts up to 300,000 job cuts over the next five years, accounting for almost 7% of total employment in these sectors. Chemistry and metallurgy. Industries are now producing 20% less than they did before 2022. RES cannot cover the required capacity. We are waiting for the German government to help the country end its energy and economic suicide. #Germany #Chemistry #Government 🇪🇺 Keep up with the latest Star Union News 🖥

75 802

Data Science Roadmap: 🗺 📂 Math & Stats ∟📂 Python/R ∟📂 Data Wrangling ∟📂 Visualization ∟📂 ML ∟📂 DL & NLP ∟📂 Projects ∟ ✅ Apply For Job Like if you need detailed explanation step-by-step ❤️

75 802

𝗙𝗿𝗲𝗲 𝗧𝗖𝗦 𝗶𝗢𝗡 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀!😍 Looking to boost your career with free online courses? 🎓 TCS iON, a leading digital learning platform from Tata Consultancy Services (TCS), offers a variety of free courses across multiple domains!📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3Dc0K1S Start learning today and take your career to the next level!✅️