Available now! Telegram Research 2025 โ the year's key insights 
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
Show more๐ 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 75 820 subscribers, ranking 2 110 in the Education category and 4 270 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 75 820 subscribers.
According to the latest data from 19 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 855 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.21%. Within the first 24 hours after publication, content typically collects 1.26% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 431 views. Within the first day, a publication typically gains 953 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.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โJoin 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 20 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.
75 820
Subscribers
+1024 hours
+1447 days
+85530 days
Posts Archive
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1: How would you preprocess and tokenize text data from tweets for sentiment analysis? Discuss potential challenges and solutions.
- Answer: Preprocessing and tokenizing text data for sentiment analysis involves tasks like lowercasing, removing stop words, and stemming or lemmatization. Handling challenges like handling emojis, slang, and noisy text is crucial. Tools like NLTK or spaCy can assist in these tasks.
2: Explain the collaborative filtering approach in building recommendation systems. How might Twitter use this to enhance user experience?
- Answer: Collaborative filtering recommends items based on user preferences and similarities. Techniques include user-based or item-based collaborative filtering and matrix factorization. Twitter could leverage user interactions to recommend tweets, users, or topics.
3: Write a Python or Scala function to count the frequency of hashtags in a given collection of tweets.
- Answer (Python):
def count_hashtags(tweet_collection):
hashtags_count = {}
for tweet in tweet_collection:
hashtags = [word for word in tweet.split() if word.startswith('#')]
for hashtag in hashtags:
hashtags_count[hashtag] = hashtags_count.get(hashtag, 0) + 1
return hashtags_count
Getting Started in the Stock Market: A Beginner's Guide
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Repost from Coding Projects
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Basics of Machine Learning ๐๐
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Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
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Software Engineers vs AI Engineers: ๐
Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.
1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a โNoviceโ is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.
The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.
Here is an example.
Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. ๐
Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.
In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."
Docker_for_Data_Science_Building_Scalable_and_Extensible_Data_Infrastructure.epub1.84 MB
10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
Accelerate Deep Learning Workloads with Amazon SageMaker
Vadim Dabravolski, 2022
