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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
Ko'proq ko'rsatish📈 Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 822 obunachidan iborat bo'lib, Taʼlim toifasida 2 109-o'rinni va Hindiston mintaqasida 4 254-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 75 822 obunachiga ega bo‘ldi.
20 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 833 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.15% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.15% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 391 marta ko‘riladi; birinchi sutkada odatda 875 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 21 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
75 822
Obunachilar
+124 soatlar
+1047 kunlar
+83330 kunlar
Postlar arxiv
matt-harrison-effective-pandas-patterns-for-data-2021.pdf38.05 MB
Repost from Coding Interview Resources
Data Science Interview questions.pdf17.59 MB
1. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
2. When does regularization come into play in Machine Learning?
At times when the model begins to underfit or overfit, regularization becomes necessary. It is a regression that diverts or regularizes the coefficient estimates towards zero. It reduces flexibility and discourages learning in a model to avoid the risk of overfitting. The model complexity is reduced and it becomes better at predicting.
3. How can we relate standard deviation and variance?
Standard deviation refers to the spread of your data from the mean. Variance is the average degree to which each point differs from the mean i.e. the average of all data points. We can relate Standard deviation and Variance because it is the square root of Variance.
4. What is the exploding gradient problem while using the back propagation technique?
When large error gradients accumulate and result in large changes in the neural network weights during training, it is called the exploding gradient problem. The values of weights can become so large as to overflow and result in NaN values. This makes the model unstable and the learning of the model to stall just like the vanishing gradient problem.
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1. What do you understand by a random forest model?
It combines multiple models together to get the final output or, to be more precise, it combines multiple decision trees together to get the final output. So, decision trees are the building blocks of the random forest model.
2. How are Data Science and Machine Learning related to each other?
Data Science and Machine Learning are two terms that are closely related but are often misunderstood. Both of them deal with data. Data Science is a broad field that deals with large volumes of data and allows us to draw insights out of this voluminous data. Machine Learning, on the other hand, can be thought of as a sub-field of Data Science. It also deals with data, but here, we are solely focused on learning how to convert the processed data into a functional model, which can be used to map inputs to outputs, e.g., a model that can expect an image as an input and tell us if that image contains a flower as an output.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
4. Explain TF/IDF vectorization.
The expression ‘TF/IDF’ stands for Term Frequency–Inverse Document Frequency. It is a numerical measure that allows us to determine how important a word is to a document in a collection of documents called a corpus. TF/IDF is used often in text mining and information retrieval.
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Certified_Kubernetes_Security_Specialist_CKS_Study_Guide_Third_Early.epub6.72 MB
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Deep_Learning_by_Ian_Goodfellow,_Yoshua_Bengio,_and_Aaron_Courville.pdf14.99 MB
Mathematical Foundations of Data Science Using R
Frank Emmert-Streib, 2020
