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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
Ko'proq ko'rsatish๐ Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 810 obunachidan iborat bo'lib, Taสผlim toifasida 2 118-o'rinni va Hindiston mintaqasida 4 300-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 75 810 obunachiga ega boโldi.
17 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 903 ga, soโnggi 24 soatda esa 2 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oโrtacha 3.39% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.40% ini tashkil etuvchi reaksiyalarni toโplaydi.
- Post qamrovi: Har bir post oโrtacha 2 573 marta koโriladi; birinchi sutkada odatda 1 064 ta koโrish yigโiladi.
- Reaksiyalar va oโzaro taโsir: Auditoriya faol: har bir postga oโrtacha 4 ta reaksiya keladi.
- Tematik yoโnalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
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
Yuqori yangilanish chastotasi (oxirgi maโlumot 18 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taสผlim toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
75 810
Obunachilar
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Postlar arxiv
5 Python functions for statistical analysis:
๐น mean(): Calculates the average of your data. Perfect for understanding central tendencies.
๐น median(): Finds the middle value in your data. Useful when your data has outliers.
๐น mode(): Identifies the most frequent value. Key for categorical data analysis.
๐น std(): Computes the standard deviation. Crucial for measuring data dispersion.
๐น var(): Calculates the variance. Helps in understanding data variability. DataAnalytics
Guesstimate questions are scary, simply because they really matter for impacting your performance in those all-important interviews โ often for consulting, data analytics or product management. No need to worry; you can do it! In this guide, we are looking at how to approach guesstimate questions with confidence and make what sounds like a guessing game into an opportunity for showcasing our analytical thinking
๐๐
https://datasimplifier.com/guesstimate-questions/
Data Analyst vs. Data Scientist ๐๐
https://t.me/sqlspecialist/775
How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ Bayes' Theorem & conditional probability
โ Permutations & combinations
โ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ Mean, median, mode
โ Standard deviation and variance
โ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ A/B experimentation
โ T-test, Z-test, Chi-squared tests
โ Type 1 & 2 errors
โ Sampling techniques & biases
โ Confidence intervals & p-values
โ Central Limit Theorem
โ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ Logistic & Linear regression
โ Decision trees & random forests
โ Clustering models
โ Feature engineering
โ Feature selection methods
โ Model testing & validation
โ Time series analysis
I have curated the best interview resources to crack Data Science Interviews
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Common Python errors and what they mean:
๐น SyntaxError: Incorrectly written code structure. Check for typos or missing punctuation (like missing '';,).
๐น IndentationError: Inconsistent use of spaces and tabs. Keep your indentation consistent.
๐น TypeError: Performing an operation on incompatible types. Like adding a string and an integer โคต๏ธ
๐น NameError: Using a variable or function that hasn't been defined. Like print(undeclared_variable)
๐น ValueError: Function receives the correct type but an inappropriate value. When you are trying to convert str to ing, like int("abc")
7. ๐ด ๐๐๐ฆ๐๐๐ฉ๐๐ก๐ง๐๐๐๐ฆ ๐ด
โข Sensitive to the choice of kernel function
โข Sensitive to the choice of regularization parameter, which determines the trade-off between finding a good boundary and avoiding overfitting.
6. ๐ข ๐๐๐ฉ๐๐ก๐ง๐๐๐๐ฆ ๐ข
โข useful when the data is not linearly separable
โข very effective in high-dimensional data and can handle a large number of features with relatively small datasets
5. To transform the data to a higher-dimensional space, SVMs use what is called ๐ธ๐ฒ๐ฟ๐ป๐ฒ๐น ๐ณ๐๐ป๐ฐ๐๐ถ๐ผ๐ป๐.
There are two main types:
1๏ธโฃ Polynomial kernels
2๏ธโฃ Radial kernels
4. But letโs go back to finding the boundaries...
To overcome linear limitations, SVMs take the data and project it into a higher-dimensional space, where finding the boundary becomes much easier.
This boundary is called the maximum margin hyperplane.
3. For data with non-linear relationships, finding a boundary is impossible. This boundary is called ๐๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ป๐ด ๐ต๐๐ฝ๐ฒ๐ฟ๐ฝ๐น๐ฎ๐ป๐ฒ.
The points closest to this boundary, named ๐๐๐ฝ๐ฝ๐ผ๐ฟ๐ ๐๐ฒ๐ฐ๐๐ผ๐ฟ๐, play a key role in shaping the SVMโs decision-making process.
2. Its goal is to find a boundary that maximally separates the data into different classes (classification) or fits the data with a line/plane (regression).
They excel at handling intricate datasets where finding the right boundary seems challenging.
Support Vector Machines clearly explained๐
1. Support Vector Machine is a useful Machine Learning algorithm frequently used for both classification and regression problems.
โญ this is a ๐๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฎ๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ.
Basically, they need labels or targets to learn!
6 essential Python functions for file handling:
๐น open(): Opens a file and returns a file object. Essential for reading and writing files
๐น read(): Reads the contents of a file
๐น write(): Writes data to a file. Great for saving output
๐น close(): Closes the file
๐น with open(): Context manager for file operations. Ensures proper file handling
๐น pd.read_excel(): Reads Excel files into a pandas DataFrame. Crucial for working with Excel data
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
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Like if you need similar content ๐๐
