Data Science
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Data science, Machine learning, and Artificial Intelligence. We post daily contents related to machine learning focusing on Numpy, Pandas, and ML effectively.
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📈 Analytical overview of Telegram channel Data Science
Channel Data Science (@data_science_ai_python) in the English language segment is an active participant. Currently, the community unites 65 459 subscribers, ranking 1 377 in the Technologies & Applications category.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 65 459 subscribers.
According to the latest data from 02 December, 2024, the channel demonstrates stable activity. Although there has been a change in the number of participants by 82 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Data science, Machine learning, and Artificial Intelligence. We post daily contents related to machine learning focusing on Numpy, Pandas, and ML effectively.”
Thanks to the high frequency of updates (latest data received on 03 December, 2024), 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 Technologies & Applications category.
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Channel Posts
+5
Howdy everyone 👋👋
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How about continuing our discussion on how to use Pandas to get valuable insights from our data? Shall we? 👌
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👨💻#Pandas
| 2 |  +5Hey everyone 👋
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As data scientists, we are data hungry!! Good news is data is available everywhere on the internet, and Pandas has the feature to import all of that goodness easily into a DataFrame 👌
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How? Check out the slides!!
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👨💻#Pandas | 13 286 |
| 3 |  +2Lesson of the day: apply() in Pandas 💪🎉 with the help of our favorite founders who are back yet again!!
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apply() allows you to “apply” 😮 any user defined function to column(s) of data 👌
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Swipe to see what we were curious to find out about our founders using apply() 🔍 .
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👨💻#Pandas | 11 469 |
| 4 |  Today, we are gonna talk about:
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assign()
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assign() lets do create a new column from a different column with some modification 💪
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Here we are subtracting our founders’ birth year from the current year to find their ages +/- 1 year 👍
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Later, we use the mean() function we covered in Part 3 of these series to find that together our favorite founders are 51.5 years young ‼️
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👨💻#Pandas | 11 415 |
| 5 |  Welcome to part 5 of Fun with Pandas featuring our founders 👌
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Fast and robust string operations are crucial to a large data framework. As shown, one can split columns based on unique patterns of their values .
Here, the separation of city from state/province and country is possible through finding the common pattern of “, “ between city and state and between state and country
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At the end, we are curious to know more about our South African born founder so we filter on South Africa 🇿🇦 .
No hard feelings Bill, Mark, and Jeff. We are curious about you too 😍
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👨💻#Pandas | 12 097 |
| 6 |  +4Part 10 of Intro to Machine Learning Series
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👨💻 #Machine_Learning | 22 752 |
| 7 | +4 Part 9 of Intro to Machine Learning Series
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👨💻 #Machine_Learning | 24 977 |
| 8 | +4 Part 8 of Intro to Machine Learning Series
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👨💻 #Machine_Learning | 24 551 |
| 9 | +4 Neural networks are at the center of attention for machine learning 🙏.So it’s important to get introduced early on our journey 👍..🗣
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👨💻 #Machine_Learning | 21 937 |
| 10 | What do you say we learn some machine learning today? 👌🎉.Linear regression is typically one of the first machine learning algorithms we hear about when we start learning about ML in general.Disguised in a simple intuitive algorithm, there exists a series of foundational concepts in ML 🤔.To find a loss function whose minimization gives the problem the optimal result.To visualize that loss function and how to minimize it 👌👌.Did you know that companies such as Zillow and Redfin have used a flavor of Linear Regression to predict prices of homes?!! 😱
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👨💻 #Machine_Learning | 21 742 |
| 11 | Join Our Community @Channels | 8 527 |
| 12 | Hi everyone 👋👋.Hope you’re having a nice weekend 👌.Today we are continuing our journey with the “intro to machine learning” series.And of course, SVMs 🎉.SVM or support vector machine is a machine learning algorithm whose job is to find a hyperplane that divides the data for the each label 👍.The power of SVMs comes through especially when the data is non linear or not easily distinguishable by eye!.SVMs use non linear kernels to transform the data to another space where the resulting data points are easily divisible by a hyperplane and then they transform everything including the hyperplane back to the initial dimensions.How cool is that with the right kernel, the SVM in the post was able to figure out the circular pattern of our data points? 👏👏.I personally think it’s absolutely amazing!!.Let me know what you think below.
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👨💻 #Machine_Learning | 7 711 |
| 13 | Hi everyone 👋👋 and welcome to another “Intro to Machine Learning” post 🧠.Supervise Learning is everywhere. In fact, 90% of the problems I have solved so far with ML have been through Supervised Learning.With Supervised Learning, you can answer so many questions and become an expert in ML 👌.The two types of Supervised Learning are crucial to Artificial Intelligence: regression and classification.An example of regression is predictions the price of a home based on its number of bedrooms, number of bathrooms, size and age 🏠 ..An example of a classification problem could be predicting whether a cancer tumor is benign or not 💊 .
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👨💻 #Machine_Learning | 5 224 |
| 14 | Hello everyone 👋👋.Today we are continuing our journey in machine learning with this 3rd post in the series 👍.Logistic regression is one of the most widely used and popular classification algorithms out there. Due to its diversity, simplicity and robustness, it’s become super popular as a baseline model all along the field 👌.At the heart of logistic regression, is the sigmoid function, a smooth function that takes any value and outputs a value between 0 and 1. This function allows for any input to be “classified” in one of the two binary classes after a threshold is applied 👏.Neat, right? 😍
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👨💻 #Machine_Learning | 5 601 |
| 15 | Hello data science enthusiasts 👋👋.Weekend calls for a machine learning related post, doesn’t it?.Machine learning historically started with the two main types: supervised and unsupervised..Overtime, a new type was invented “reinforcement” learning, and now there is even more types ....So, what are they?.Supervised: training a model with labeled data points, you “supervise” the model by giving it the “right answers”.Unsupervised: you ask the model to tell you what it thinks the data classifications or clustering should be based on the pattern it can find in the data. This is a good approach for when there are no right answers or the right answers are not available..Reinforcement: this type is largely evolving and generally is orchestrated on a series of actions and rewards. The model learns over time what action to take and when to optimize its total rewards..Machine learning is fast moving field and the research in it brings a ton of new ideas every month 👌🧠.We should be covering the different techniques used on this slide in the future posts so stay tuned 🗣🗣
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👨💻 #Machine_Learning | 4 552 |
| 16 | Hello all and welcome to the post of the day 🙏 .Today, we are going to introduce Machine Learning and Deep Learning and talk about what makes them different 🤔 .In traditional machine learning, scientists had to define concrete and well defined features for the inputs, those features would then get fed into a neural network that would produce a prediction 👍.In deep learning however, we are leaving it to the network to learn and ultimately decide which features it seems relevant to the learning problem 💡 .This is precisely why deep learning is so powerful, everything end to end is learned by the network. The hard part then becomes designing the perfect network for a given problem 🧠.Super excited to be going through this journey through AI with you guys. Stay tuned for more machine learning posts this coming week 🎉
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👨💻 #Machine_Learning | 5 800 |
| 17 | Hi data scientists 👋👋👋.A coincidence that the day we just finished was Valentine’s Day but I have been receiving a lot of love 💙 from you guys lately! Many of you have reached out and supported the content, just know that it’s appreciated and it will make this page better! 👌.With that, let’s get to today’s post, shall we?? .Of course when we are talking about Pandas, our good friends the founders are back to help us! 🎉.However our founders have been having a little argument lately. Even one was allegedly heard calling another one “hey boomer” ‼️ and the other responded back with “you millennial” 🤦🏻♂️ Even though arguments are not nice, this gives us the chance to use Pandas to settle who is in what generation!.pd.cut allows us to categorize a continuous spectrum into bins 👌 here our bins are the generations and the continuous spectrum is the year number 👍.After seeing exactly who’s in what generation, our founders realize that they should apologize to each other. They have promised to treat each other better in the next post so stay tuned 🗣.Correction: founders_df[“Birth”] should be founders_df[“BirthYear”] ‼️‼️
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👨💻#Pandas | 11 680 |
| 18 | Hello all and welcome to the 3rd episode of our Intro to Pandas series @bigdataguru 👋🙏.Our friends, the 4 founders, have been kind enough to show up once again to help us understand two important functions on Pandas 🎉.groupby()mean().Groupby() as the name suggests groups the rows of data frame based on the values of a column of columns..The result of the groupby is usually used for aggregation of data, in the case finding the mean number of employees employed in given states by these 4 companies.With those in our toolset, we can now do incredible things with data 🙌🏻🙌🏻.
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👨💻#Pandas | 11 170 |
| 19 | Hi everyone 👋👋.My friends are here again for part 2 of our intro to Pandas👌🎉👏.In Pandas, you can easily extract more useful data points from existing data in the table, and because Pandas has been optimized to work on large amounts of data, column operations are super fast 💨..Here I divide the founders’ net worth by their age, to get a sense of their average wealth accumulation rate.Then I am interested to see who’s accumulated wealth the fastest, so I sort the column in the descending order 🙌🏻.Super fast, in a few lines, I have answered a couple of my questions about my favorite founders 👍.
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👨💻#Pandas | 11 059 |
| 20 | Hi everyone 👋👋.I wanted to introduce Pandas to you in case it’s new to you. We will be working a lot with it in the future so a nice introduction will go a long way 🙌.I have asked a few of my friends ‼️ to help me introduce Pandas to you by showing up on the post 😂😂.Jokes aside, Pandas is a really powerful data analytics library in Python that I use almost everyday. It’s robust, fast, and great for prototyping data science problems 🧠..It quickly feels like you’re working with a database, so if you know SQL this won’t feel too different..Let me know who your favorite founder is from the 4 on the picture. I’ll keep mine a secret for now. 👍
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👨💻#Pandas | 11 745 |
