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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Network:📈 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 77 386 subscribers, ranking 1 996 in the Education category and 3 950 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 386 subscribers.
According to the latest data from 03 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 345 over the last 30 days and by 14 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.54%. Within the first 24 hours after publication, content typically collects 1.07% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 965 views. Within the first day, a publication typically gains 826 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- 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 04 September, 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.
77 386
Subscribers
+1424 hours
+1047 days
+34530 days
Posts Archive
𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
- Python Programming
- Data Analytics
- Generative AI
- Machine Learning
- Data Science
- SQL
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𝗟𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
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🚀 Roadmap to Become a Machine Learning Engineer 💻
📂 Programming Basics
∟📂 Master Python & OOP
∟📂 Learn Data Structures & Algorithms
∟📂 Master Git & Version Control
📂 Mathematics for ML
∟📂 Linear Algebra & Calculus
∟📂 Probability & Statistics
∟📂 Optimization Techniques
📂 Data Handling & Processing
∟📂 Work with Pandas & NumPy
∟📂 Data Cleaning & Preprocessing
∟📂 Feature Engineering & Selection
📂 Machine Learning Fundamentals
∟📂 Understand Supervised & Unsupervised Learning
∟📂 Master Scikit-Learn & ML Algorithms
∟📂 Model Training, Evaluation & Tuning
📂 Deep Learning & Neural Networks
∟📂 Learn TensorFlow & PyTorch
∟📂 Build & Train Neural Networks
∟📂 Master CNNs, RNNs & Transformers
📂 ML System Deployment
∟📂 Learn Model Deployment (Flask, FastAPI)
∟📂 Work with MLOps & Cloud Platforms
∟📂 Deploy Models to Production
📂 Projects & Real-World Applications
∟📂 Build End-to-End ML Projects
∟📂 Work on Open-Source Contributions
∟📂 Showcase on GitHub & Kaggle
📂 Interview Preparation & Job Hunting
∟📂 Solve ML Coding Challenges
∟📂 Learn System Design for ML
∟📂 Network & Apply for Jobs
✅️ Get Hired
React "❤️" for More 👨💻
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.me/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.me/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
𝗬𝗼𝘂𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
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𝐋𝐢𝐧𝐤👇:-
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Build Real-World AI Projects & stand out from the crowd!✅️
5 Innovative Ways to Elevate Your Data Science Project
Guys, when working on a data science project, the usual approach is to clean the data, apply a model, and optimize it. But if you really want to stand out, you need to think beyond standard practices! Here are 5 innovative strategies to take your project to the next level:
1️⃣ Multi-Model Fusion: Blend Different Algorithms
🔹 Instead of relying on a single model, try combining multiple models (ensemble learning) to improve accuracy.
🔹 Example: Mix a Decision Tree with a Neural Network to capture both rule-based and deep-learning insights.
2️⃣ Dynamic Feature Engineering with AutoML
🔹 Instead of manually creating new features, use Automated Machine Learning (AutoML) to generate the best transformations.
🔹 Example: FeatureTools in Python can automatically create powerful new features from your raw data.
3️⃣ Real-Time Data Streaming for Live Insights
🔹 Instead of static datasets, work with real-time data using Kafka or Apache Spark Streaming.
🔹 Example: In a stock market prediction model, process live trading data instead of historical prices only.
4️⃣ Explainability with AI (XAI)
🔹 Use SHAP or LIME to explain your model’s decisions and make it interpretable.
🔹 Example: Show why your credit risk model rejected a loan application with feature importance scores.
5️⃣ Gamify Your Data Visualization
🔹 Instead of boring static graphs, create interactive visualizations using D3.js or Plotly to engage users.
🔹 Example: Build a dynamic dashboard where users can tweak inputs and see real-time predictions.
🚀 Pro Tip: Always document your experiments, compare results, and keep testing new approaches!
#datascience
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