From 26962c870c8284be2507058872e0ed215e14d79c Mon Sep 17 00:00:00 2001 From: Kuldeep Singh Sidhu Date: Wed, 14 Aug 2024 12:39:01 +0000 Subject: [PATCH] Deployed fe7acbd with MkDocs version: 1.6.0 --- 404.html | 2 +- Cheat-Sheets/Django/index.html | 2 +- Cheat-Sheets/Flask/index.html | 2 +- Cheat-Sheets/Hypothesis-Tests/index.html | 4 ++-- Cheat-Sheets/Keras/index.html | 2 +- Cheat-Sheets/NumPy/index.html | 2 +- Cheat-Sheets/Pandas/index.html | 2 +- Cheat-Sheets/PySpark/index.html | 2 +- Cheat-Sheets/PyTorch/index.html | 2 +- Cheat-Sheets/Python/index.html | 2 +- Cheat-Sheets/RegEx/index.html | 2 +- Cheat-Sheets/SQL/index.html | 2 +- Cheat-Sheets/Sk-learn/index.html | 2 +- Cheat-Sheets/tensorflow/index.html | 2 +- Deploying-ML-models/deploying-ml-models/index.html | 4 ++-- Interview-Questions/Natural-Language-Processing/index.html | 2 +- Interview-Questions/Probability/index.html | 4 ++-- Interview-Questions/System-design/index.html | 2 +- Interview-Questions/data-structures-algorithms/index.html | 4 ++-- Machine-Learning/ARIMA/index.html | 2 +- Machine-Learning/Activation functions/index.html | 2 +- Machine-Learning/Collaborative Filtering/index.html | 2 +- Machine-Learning/Confusion Matrix/index.html | 2 +- Machine-Learning/DBSCAN/index.html | 2 +- Machine-Learning/Decision Trees/index.html | 2 +- Machine-Learning/Gradient Boosting/index.html | 2 +- Machine-Learning/K-means clustering/index.html | 2 +- Machine-Learning/Linear Regression/index.html | 2 +- Machine-Learning/Logistic Regression/index.html | 2 +- Machine-Learning/Loss Function MAE, RMSE/index.html | 2 +- Machine-Learning/Neural Networks/index.html | 2 +- Machine-Learning/Normal Distribution/index.html | 2 +- Machine-Learning/Normalization Regularisation/index.html | 2 +- Machine-Learning/Overfitting, Underfitting/index.html | 2 +- Machine-Learning/PCA/index.html | 2 +- Machine-Learning/Random Forest/index.html | 2 +- Machine-Learning/Support Vector Machines/index.html | 2 +- Machine-Learning/Unbalanced, Skewed data/index.html | 2 +- Machine-Learning/kNN/index.html | 2 +- Online-Material/Online-Material-for-Learning/index.html | 2 +- Online-Material/popular-resouces/index.html | 2 +- Suggested-Learning-Paths/index.html | 2 +- as-fast-as-possible/Deep-CV/index.html | 2 +- as-fast-as-possible/Deep-NLP/index.html | 2 +- as-fast-as-possible/Neural-Networks/index.html | 2 +- as-fast-as-possible/TF2-Keras/index.html | 2 +- as-fast-as-possible/index.html | 2 +- index.html | 4 ++-- projects/index.html | 2 +- 49 files changed, 54 insertions(+), 54 deletions(-) diff --git a/404.html b/404.html index 93cc9d2..9f5638e 100644 --- a/404.html +++ b/404.html @@ -1 +1 @@ - Data Science Interview preparation

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Django

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Django

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Flask

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Flask

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Hypothesis Tests in Python

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters.

Few Notes:

  • When it comes to assumptions such as the expected distribution of data or sample size, the results of a given test are likely to degrade gracefully rather than become immediately unusable if an assumption is violated.
  • Generally, data samples need to be representative of the domain and large enough to expose their distribution to analysis.
  • In some cases, the data can be corrected to meet the assumptions, such as correcting a nearly normal distribution to be normal by removing outliers, or using a correction to the degrees of freedom in a statistical test when samples have differing variance, to name two examples.

Normality Tests

This section lists statistical tests that you can use to check if your data has a Gaussian distribution.

Gaussian distribution (also known as normal distribution) is a bell-shaped curve.

Shapiro-Wilk Test

Tests whether a data sample has a Gaussian distribution/Normal distribution.

Source: https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/

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Keras

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Keras

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NumPy

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NumPy

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Pandas

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Pandas

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PySpark

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PySpark

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PyTorch

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PyTorch

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Python

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Python

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Regular Expressions (RegEx)

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Regular Expressions (RegEx)

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SQL

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SQL

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Scikit Learn

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Scikit Learn

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TensorFlow

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TensorFlow

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Introduction

This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.

Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.

This platform is maintained by you! 🤗 You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews.

Contribute to the platform

Contribution in any form will be deeply appreciated. 🙏

Add questions

❓ Add your questions here. Please ensure to provide a detailed description to allow your fellow contributors to understand your questions and answer them to your satisfaction.

Add New question

🤝 Please note that as of now, you cannot directly add a question via a pull request. This will help us to maintain the quality of the content for you.

Add answers/topics

📝 These are the answers/topics that need your help at the moment

  • Add documentation for the project
  • Online Material for Learning
  • Suggested Learning Paths
  • Cheat Sheets
    • Django
    • Flask
    • Numpy
    • Pandas
    • PySpark
    • Python
    • RegEx
    • SQL
  • NLP Interview Questions
  • Add python common DSA interview questions
  • Add Major ML topics
    • Linear Regression
    • Logistic Regression
    • SVM
    • Random Forest
    • Gradient boosting
    • PCA
    • Collaborative Filtering
    • K-means clustering
    • kNN
    • ARIMA
    • Neural Networks
    • Decision Trees
    • Overfitting, Underfitting
    • Unbalanced, Skewed data
    • Activation functions relu/ leaky relu
    • Normalization
    • DBSCAN
    • Normal Distribution
    • Precision, Recall
    • Loss Function MAE, RMSE
  • Add Pandas questions
  • Add NumPy questions
  • Add TensorFlow questions
  • Add PyTorch questions
  • Add list of learning resources

Report/Solve Issues

Issues

🔧 To report any issues find me on LinkedIn or raise an issue on GitHub.

🛠 You can also solve existing issues on GitHub and create a pull request.

Say Thanks

😊 If this platform helped you in any way, it would be great if you could share it with others.

Check out this 👇 platform 👇 for data science content:
+ Home - Data Science Interview preparation      
  
 
  
 
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
       
Home
 
  Go to website  
 
Introduction
 
This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.
 
Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.
 
This platform is maintained by you! 🤗 You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews.
 
Contribute to the platform
 
Contribution in any form will be deeply appreciated. 🙏
 
Add questions
 
❓ Add your questions here. Please ensure to provide a detailed description to allow your fellow contributors to understand your questions and answer them to your satisfaction.
 
Add New question
 
🤝 Please note that as of now, you cannot directly add a question via a pull request. This will help us to maintain the quality of the content for you.
 
Add answers/topics
 
📝 These are the answers/topics that need your help at the moment
 
     
  •  Add documentation for the project
    •  
  •  Online Material for Learning
    •  
  •  Suggested Learning Paths
    •  
  •  Cheat Sheets
       
    •  Django
      •  
    •  Flask
      •  
    •  Numpy
      •  
    •  Pandas
      •  
    •  PySpark
      •  
    •  Python
      •  
    •  RegEx
      •  
    •  SQL
      •  
     
    •  
  •  NLP Interview Questions
    •  
  •  Add python common DSA interview questions
    •  
  •  Add Major ML topics
       
    •  Linear Regression 
      •  
    •  Logistic Regression 
      •  
    •  SVM 
      •  
    •  Random Forest 
      •  
    •  Gradient boosting 
      •  
    •  PCA 
      •  
    •  Collaborative Filtering 
      •  
    •  K-means clustering 
      •  
    •  kNN 
      •  
    •  ARIMA 
      •  
    •  Neural Networks 
      •  
    •  Decision Trees 
      •  
    •  Overfitting, Underfitting
      •  
    •  Unbalanced, Skewed data
      •  
    •  Activation functions relu/ leaky relu
      •  
    •  Normalization
      •  
    •  DBSCAN 
      •  
    •  Normal Distribution 
      •  
    •  Precision, Recall 
      •  
    •  Loss Function MAE, RMSE 
      •  
     
    •  
  •  Add Pandas questions
    •  
  •  Add NumPy questions
    •  
  •  Add TensorFlow questions
    •  
  •  Add PyTorch questions
    •  
  •  Add list of learning resources
    •  
 
Report/Solve Issues
 
Issues
 
🔧 To report any issues find me on LinkedIn or raise an issue on GitHub.
 
🛠 You can also solve existing issues on GitHub and create a pull request.
 
Say Thanks
 
😊 If this platform helped you in any way, it would be great if you could share it with others.
 
  
 
Check out this 👇 platform 👇 for data science content:
 👉 https://singhsidhukuldeep.github.io/data-science-interview-prep/ 👈
 
 #data-science #machine-learning #interview-preparation 
@@ -16,4 +16,4 @@
 
 
venv\Scripts\deactivate
 
 
To install the latest
 
pip3 install mkdocs
 pip3 install mkdocs-material
-
 
Useful Commands
 
     
  • mkdocs serve - Start the live-reloading docs server.
    •  
  • mkdocs build - Build the documentation site.
    •  
  • mkdocs -h - Print help message and exit.
    •  
  • mkdocs gh-deploy - Use mkdocs gh-deploy --help to get a full list of options available for the gh-deploy command. Be aware that you will not be able to review the built site before it is pushed to GitHub. Therefore, you may want to verify any changes you make to the docs beforehand by using the build or serve commands and reviewing the built files locally.
    •  
  • mkdocs new [dir-name] - Create a new project. No need to create a new project
    •  
 
Useful Documents
  
FAQ
 
     
  •  
    Can I filter questions based on companies? 🤪
     
    As much as this platform aims to help you with your interview preparation, it is not a short-cut to crack one. Think of this platform as a practicing field to help you sharpen your skills for your interview processes. However, for your convenience we have sorted all the questions by topics for you. 🤓
     
    This doesn't mean that such feature won't be added in the future. "Never say Never"
     
    But as of now there is neither plan nor data to do so. 😢
     
    •  
  •  
    Why is this platform free? 🤗
     
    Currently there is no major cost involved in maintaining this platform other than time and effort that is put in by every contributor. If you want to help you can contribute here. 
     
    If you still want to pay for something that is free, we would request you to donate it to a charity of your choice instead. 😇
     
    •  
 
Credits
 
Maintained by
 
👨‍🎓 Kuldeep Singh Sidhu 
 
Github: github/singhsidhukuldeep https://github.com/singhsidhukuldeep
 
Website: Kuldeep Singh Sidhu (Website) http://kuldeepsinghsidhu.com
 
LinkedIn: Kuldeep Singh Sidhu (LinkedIn) https://www.linkedin.com/in/singhsidhukuldeep/
 
Contributors
 
😎 The full list of all the contributors is available here
 
Current Status
 
Maintenance Website shields.io GitHub pages status GitHub up-time BOT Commits DependaBot
 
Issues Total Commits Contributors Forks Stars Watchers Branches
 
License: AGPL v3 made-with-python made-with-Markdown repo- size Followers
  
 
  
 
  
 
 
 
    
\ No newline at end of file
+

Useful Commands

  • mkdocs serve - Start the live-reloading docs server.
  • mkdocs build - Build the documentation site.
  • mkdocs -h - Print help message and exit.
  • mkdocs gh-deploy - Use mkdocs gh-deploy --help to get a full list of options available for the gh-deploy command. Be aware that you will not be able to review the built site before it is pushed to GitHub. Therefore, you may want to verify any changes you make to the docs beforehand by using the build or serve commands and reviewing the built files locally.
  • mkdocs new [dir-name] - Create a new project. No need to create a new project

Useful Documents

FAQ

  • Can I filter questions based on companies? 🤪

    As much as this platform aims to help you with your interview preparation, it is not a short-cut to crack one. Think of this platform as a practicing field to help you sharpen your skills for your interview processes. However, for your convenience we have sorted all the questions by topics for you. 🤓

    This doesn't mean that such feature won't be added in the future. "Never say Never"

    But as of now there is neither plan nor data to do so. 😢

  • Why is this platform free? 🤗

    Currently there is no major cost involved in maintaining this platform other than time and effort that is put in by every contributor. If you want to help you can contribute here.

    If you still want to pay for something that is free, we would request you to donate it to a charity of your choice instead. 😇

Credits

Maintained by

👨‍🎓 Kuldeep Singh Sidhu

Github: github/singhsidhukuldeep https://github.com/singhsidhukuldeep

Website: Kuldeep Singh Sidhu (Website) http://kuldeepsinghsidhu.com

LinkedIn: Kuldeep Singh Sidhu (LinkedIn) https://www.linkedin.com/in/singhsidhukuldeep/

Contributors

😎 The full list of all the contributors is available here

Current Status

Maintenance Website shields.io GitHub pages status GitHub up-time BOT Commits DependaBot

Issues Total Commits Contributors Forks Stars Watchers Branches

License: AGPL v3 made-with-python made-with-Markdown repo- size Followers

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NLP Interview Questions

Total Questions Unanswered Questions Answered Questions

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NLP Interview Questions

Total Questions Unanswered Questions Answered Questions

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Probability Interview Questions

Total Questions Unanswered Questions Answered Questions

Average score on a dice role of at most 3 times

Question

Consider a fair 6-sided dice. Your aim is to get the highest score you can, in at-most 3 roles.

A score is defined as the number that appears on the face of the dice facing up after the role. You can role at most 3 times but every time you role it is up to you to decide whether you want to role again.

The last score will be counted as your final score.

  • Find the average score if you rolled the dice only once?
  • Find the average score that you can get with at most 3 roles?
  • If the dice is fair, why is the average score for at most 3 roles and 1 role not the same?

Answer

If you role a fair dice once you can get:

Score Probability
1 ⅙
2 ⅙
3 ⅙
4 ⅙
5 ⅙
6 ⅙

So your average score with one role is:

sum of(score * scores's probability) = (1+2+3+4+5+6)*(⅙) = (21/6) = 3.5

The average score if you rolled the dice only once is 3.5

For at most 3 roles, let's try back-tracking. Let's say just did your second role and you have to decide whether to do your 3rd role!

We just found out if we role dice once on average we can expect score of 3.5. So we will only role the 3rd time if score on 2nd role is less than 3.5 i.e (1,2 or 3)

Possibilities

2nd role score Probability 3rd role score Probability
1 ⅙ 3.5 ⅙
2 ⅙ 3.5 ⅙
3 ⅙ 3.5 ⅙
4 ⅙ NA We won't role
5 ⅙ NA 3rd time if we
6 ⅙ NA get score >3 on 2nd

So if we had 2 roles, average score would be:

[We role again if current score is less than 3.4]
+ Probability Questions - Data Science Interview preparation      
  
 
  
 
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
       
Probability Interview Questions
 
Total Questions Unanswered Questions Answered Questions
 
 
Average score on a dice role of at most 3 times
 
Question
 
Consider a fair 6-sided dice. Your aim is to get the highest score you can, in at-most 3 roles.
 
A score is defined as the number that appears on the face of the dice facing up after the role. You can role at most 3 times but every time you role it is up to you to decide whether you want to role again.
 
The last score will be counted as your final score.
 
     
  • Find the average score if you rolled the dice only once?
    •  
  • Find the average score that you can get with at most 3 roles?
    •  
  • If the dice is fair, why is the average score for at most 3 roles and 1 role not the same?
    •  
 
Answer
 
If you role a fair dice once you can get:
 
  
 Score Probability 
   
 1 ⅙ 
 
 2 ⅙ 
 
 3 ⅙ 
 
 4 ⅙ 
 
 5 ⅙ 
 
 6 ⅙ 
  
 
So your average score with one role is: 
 
sum of(score * scores's probability) = (1+2+3+4+5+6)*(⅙) = (21/6) = 3.5
 
The average score if you rolled the dice only once is 3.5
 
For at most 3 roles, let's try back-tracking. Let's say just did your second role and you have to decide whether to do your 3rd role!
 
We just found out if we role dice once on average we can expect score of 3.5. So we will only role the 3rd time if score on 2nd role is less than 3.5 i.e (1,2 or 3)
 
Possibilities
 
  
 2nd role score Probability 3rd role score Probability 
   
 1 ⅙ 3.5 ⅙ 
 
 2 ⅙ 3.5 ⅙ 
 
 3 ⅙ 3.5 ⅙ 
 
 4 ⅙ NA We won't role 
 
 5 ⅙ NA 3rd time if we 
 
 6 ⅙ NA get score >3 on 2nd 
  
 
So if we had 2 roles, average score would be:
 
[We role again if current score is less than 3.4]
 (3.5)*(1/6) + (3.5)*(1/6) + (3.5)*(1/6) 
 +
 (4)*(1/6) + (5)*(1/6) + (6)*(1/6) [Decide not to role again]
@@ -10,4 +10,4 @@
 (5)*(1/6) + (6)*(1/6) [[Decide not to role again]
 =
 17/6 + 11/6 = 4.66
-
 The average score if you rolled the dice only once is 4.66
 
The average score for at most 3 roles and 1 role is not the same because although the dice is fair the event of rolling the dice is no longer independent. The scores would have been the same if we rolled the dice 2nd and 3rd time without considering what we got in the last roll i.e. if the event of rolling the dice was independent.
 
  
 
  
 
  
 
 
 
    
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+
The average score if you rolled the dice only once is 4.66

The average score for at most 3 roles and 1 role is not the same because although the dice is fair the event of rolling the dice is no longer independent. The scores would have been the same if we rolled the dice 2nd and 3rd time without considering what we got in the last roll i.e. if the event of rolling the dice was independent.

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System Design

Total Questions Unanswered Questions Answered Questions

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System Design

Total Questions Unanswered Questions Answered Questions

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🤨 Hard

😲 Very Hard

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ARIMA

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ARIMA

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Activation functions

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Activation functions

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Collaborative Filtering

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Collaborative Filtering

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Confusion Matrix

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Confusion Matrix

\ No newline at end of file diff --git a/Machine-Learning/DBSCAN/index.html b/Machine-Learning/DBSCAN/index.html index 8c2756d..d51c8cb 100644 --- a/Machine-Learning/DBSCAN/index.html +++ b/Machine-Learning/DBSCAN/index.html @@ -1 +1 @@ - DBSCAN - Data Science Interview preparation

DBSCAN

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DBSCAN

\ No newline at end of file diff --git a/Machine-Learning/Decision Trees/index.html b/Machine-Learning/Decision Trees/index.html index 89c2745..b5bc287 100644 --- a/Machine-Learning/Decision Trees/index.html +++ b/Machine-Learning/Decision Trees/index.html @@ -1 +1 @@ - Decision Trees - Data Science Interview preparation

Decision Trees

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Decision Trees

\ No newline at end of file diff --git a/Machine-Learning/Gradient Boosting/index.html b/Machine-Learning/Gradient Boosting/index.html index 9d15468..fb5281c 100644 --- a/Machine-Learning/Gradient Boosting/index.html +++ b/Machine-Learning/Gradient Boosting/index.html @@ -1 +1 @@ - Gradient Boosting - Data Science Interview preparation

Gradient Boosting

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Gradient Boosting

\ No newline at end of file diff --git a/Machine-Learning/K-means clustering/index.html b/Machine-Learning/K-means clustering/index.html index 4d756bf..efa7cdc 100644 --- a/Machine-Learning/K-means clustering/index.html +++ b/Machine-Learning/K-means clustering/index.html @@ -1 +1 @@ - K means clustering - Data Science Interview preparation

K means clustering

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K means clustering

\ No newline at end of file diff --git a/Machine-Learning/Linear Regression/index.html b/Machine-Learning/Linear Regression/index.html index 5176e83..0e8e7c8 100644 --- a/Machine-Learning/Linear Regression/index.html +++ b/Machine-Learning/Linear Regression/index.html @@ -1 +1 @@ - Linear Regression - Data Science Interview preparation

Linear Regression

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Linear Regression

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Logistic Regression

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Logistic Regression

\ No newline at end of file diff --git a/Machine-Learning/Loss Function MAE, RMSE/index.html b/Machine-Learning/Loss Function MAE, RMSE/index.html index 87a1b56..266bedb 100644 --- a/Machine-Learning/Loss Function MAE, RMSE/index.html +++ b/Machine-Learning/Loss Function MAE, RMSE/index.html @@ -1 +1 @@ - Loss Function MAE, RMSE - Data Science Interview preparation

Loss Function MAE, RMSE

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Loss Function MAE, RMSE

\ No newline at end of file diff --git a/Machine-Learning/Neural Networks/index.html b/Machine-Learning/Neural Networks/index.html index f1178c6..cae6ae3 100644 --- a/Machine-Learning/Neural Networks/index.html +++ b/Machine-Learning/Neural Networks/index.html @@ -1 +1 @@ - Neural Networks - Data Science Interview preparation

Neural Networks

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Neural Networks

\ No newline at end of file diff --git a/Machine-Learning/Normal Distribution/index.html b/Machine-Learning/Normal Distribution/index.html index ce80e8d..2a5aa28 100644 --- a/Machine-Learning/Normal Distribution/index.html +++ b/Machine-Learning/Normal Distribution/index.html @@ -1 +1 @@ - Normal Distribution - Data Science Interview preparation

Normal Distribution

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Normal Distribution

\ No newline at end of file diff --git a/Machine-Learning/Normalization Regularisation/index.html b/Machine-Learning/Normalization Regularisation/index.html index 5c79f7d..5f0e8ee 100644 --- a/Machine-Learning/Normalization Regularisation/index.html +++ b/Machine-Learning/Normalization Regularisation/index.html @@ -1 +1 @@ - Normalization Regularisation - Data Science Interview preparation

Normalization Regularisation

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Normalization Regularisation

\ No newline at end of file diff --git a/Machine-Learning/Overfitting, Underfitting/index.html b/Machine-Learning/Overfitting, Underfitting/index.html index 2c3a165..1d6069f 100644 --- a/Machine-Learning/Overfitting, Underfitting/index.html +++ b/Machine-Learning/Overfitting, Underfitting/index.html @@ -1 +1 @@ - Overfitting, Underfitting - Data Science Interview preparation

Overfitting, Underfitting

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Overfitting, Underfitting

\ No newline at end of file diff --git a/Machine-Learning/PCA/index.html b/Machine-Learning/PCA/index.html index 2f1d003..29a1a3e 100644 --- a/Machine-Learning/PCA/index.html +++ b/Machine-Learning/PCA/index.html @@ -1 +1 @@ - PCA - Data Science Interview preparation

PCA

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PCA

\ No newline at end of file diff --git a/Machine-Learning/Random Forest/index.html b/Machine-Learning/Random Forest/index.html index 7d41ecd..f9035f9 100644 --- a/Machine-Learning/Random Forest/index.html +++ b/Machine-Learning/Random Forest/index.html @@ -1 +1 @@ - Random Forest - Data Science Interview preparation

Random Forest

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Random Forest

\ No newline at end of file diff --git a/Machine-Learning/Support Vector Machines/index.html b/Machine-Learning/Support Vector Machines/index.html index fb0f498..a5e5e90 100644 --- a/Machine-Learning/Support Vector Machines/index.html +++ b/Machine-Learning/Support Vector Machines/index.html @@ -1 +1 @@ - Support Vector Machines - Data Science Interview preparation

Support Vector Machines

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Support Vector Machines

\ No newline at end of file diff --git a/Machine-Learning/Unbalanced, Skewed data/index.html b/Machine-Learning/Unbalanced, Skewed data/index.html index 7290f22..9fcf69d 100644 --- a/Machine-Learning/Unbalanced, Skewed data/index.html +++ b/Machine-Learning/Unbalanced, Skewed data/index.html @@ -1 +1 @@ - Unbalanced, Skewed data - Data Science Interview preparation

Unbalanced, Skewed data

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Unbalanced, Skewed data

\ No newline at end of file diff --git a/Machine-Learning/kNN/index.html b/Machine-Learning/kNN/index.html index 2304476..82c97e0 100644 --- a/Machine-Learning/kNN/index.html +++ b/Machine-Learning/kNN/index.html @@ -1 +1 @@ - kNN - Data Science Interview preparation

kNN

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kNN

\ No newline at end of file diff --git a/Online-Material/Online-Material-for-Learning/index.html b/Online-Material/Online-Material-for-Learning/index.html index 9f4ff55..b973d29 100644 --- a/Online-Material/Online-Material-for-Learning/index.html +++ b/Online-Material/Online-Material-for-Learning/index.html @@ -1 +1 @@ - Online Study Material - Data Science Interview preparation

Online Study Material

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Online Study Material

\ No newline at end of file diff --git a/Online-Material/popular-resouces/index.html b/Online-Material/popular-resouces/index.html index b8dee76..0947313 100644 --- a/Online-Material/popular-resouces/index.html +++ b/Online-Material/popular-resouces/index.html @@ -1 +1 @@ - Popular Blogs - Data Science Interview preparation

Popular Blogs

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Popular Blogs

\ No newline at end of file diff --git a/Suggested-Learning-Paths/index.html b/Suggested-Learning-Paths/index.html index 19a6ce6..7a23021 100644 --- a/Suggested-Learning-Paths/index.html +++ b/Suggested-Learning-Paths/index.html @@ -1 +1 @@ - 📅 Suggested Learning Paths - Data Science Interview preparation

📅 Suggested Learning Paths

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📅 Suggested Learning Paths

\ No newline at end of file diff --git a/as-fast-as-possible/Deep-CV/index.html b/as-fast-as-possible/Deep-CV/index.html index 6ed3471..1d78c28 100644 --- a/as-fast-as-possible/Deep-CV/index.html +++ b/as-fast-as-possible/Deep-CV/index.html @@ -1 +1 @@ - Deep Computer Vision - Data Science Interview preparation

Deep Computer Vision

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Deep Computer Vision

\ No newline at end of file diff --git a/as-fast-as-possible/Deep-NLP/index.html b/as-fast-as-possible/Deep-NLP/index.html index 2c0d16b..b149332 100644 --- a/as-fast-as-possible/Deep-NLP/index.html +++ b/as-fast-as-possible/Deep-NLP/index.html @@ -1 +1 @@ - Deep Natural Language Processing - Data Science Interview preparation

Deep Natural Language Processing

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Deep Natural Language Processing

\ No newline at end of file diff --git a/as-fast-as-possible/Neural-Networks/index.html b/as-fast-as-possible/Neural-Networks/index.html index d6ea1bb..0666bea 100644 --- a/as-fast-as-possible/Neural-Networks/index.html +++ b/as-fast-as-possible/Neural-Networks/index.html @@ -1 +1 @@ - Neural Networks - Data Science Interview preparation

Neural Networks

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Neural Networks

\ No newline at end of file diff --git a/as-fast-as-possible/TF2-Keras/index.html b/as-fast-as-possible/TF2-Keras/index.html index 553557e..119762b 100644 --- a/as-fast-as-possible/TF2-Keras/index.html +++ b/as-fast-as-possible/TF2-Keras/index.html @@ -1 +1 @@ - Tensorflow 2 with Keras - Data Science Interview preparation

Tensorflow 2 with Keras

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Tensorflow 2 with Keras

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Introduction

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Introduction

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Skip to content

Home

Go to website

Introduction

This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.

Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.

This platform is maintained by you! 🤗 You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews.

Contribute to the platform

Contribution in any form will be deeply appreciated. 🙏

Add questions

❓ Add your questions here. Please ensure to provide a detailed description to allow your fellow contributors to understand your questions and answer them to your satisfaction.

Add New question

🤝 Please note that as of now, you cannot directly add a question via a pull request. This will help us to maintain the quality of the content for you.

Add answers/topics

📝 These are the answers/topics that need your help at the moment

  • Add documentation for the project
  • Online Material for Learning
  • Suggested Learning Paths
  • Cheat Sheets
    • Django
    • Flask
    • Numpy
    • Pandas
    • PySpark
    • Python
    • RegEx
    • SQL
  • NLP Interview Questions
  • Add python common DSA interview questions
  • Add Major ML topics
    • Linear Regression
    • Logistic Regression
    • SVM
    • Random Forest
    • Gradient boosting
    • PCA
    • Collaborative Filtering
    • K-means clustering
    • kNN
    • ARIMA
    • Neural Networks
    • Decision Trees
    • Overfitting, Underfitting
    • Unbalanced, Skewed data
    • Activation functions relu/ leaky relu
    • Normalization
    • DBSCAN
    • Normal Distribution
    • Precision, Recall
    • Loss Function MAE, RMSE
  • Add Pandas questions
  • Add NumPy questions
  • Add TensorFlow questions
  • Add PyTorch questions
  • Add list of learning resources

Report/Solve Issues

Issues

🔧 To report any issues find me on LinkedIn or raise an issue on GitHub.

🛠 You can also solve existing issues on GitHub and create a pull request.

Say Thanks

😊 If this platform helped you in any way, it would be great if you could share it with others.

👀 This project is in early stages of development. 🤗Please contibute content if possible!
🤝You can submit simple text/markdown content, I will format it! 🙌

Home

Go to website

Introduction

This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.

Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.

This platform is maintained by you! 🤗 You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews.

Contribute to the platform

Contribution in any form will be deeply appreciated. 🙏

Add questions

❓ Add your questions here. Please ensure to provide a detailed description to allow your fellow contributors to understand your questions and answer them to your satisfaction.

Add New question

🤝 Please note that as of now, you cannot directly add a question via a pull request. This will help us to maintain the quality of the content for you.

Add answers/topics

📝 These are the answers/topics that need your help at the moment

  • Add documentation for the project
  • Online Material for Learning
  • Suggested Learning Paths
  • Cheat Sheets
    • Django
    • Flask
    • Numpy
    • Pandas
    • PySpark
    • Python
    • RegEx
    • SQL
  • NLP Interview Questions
  • Add python common DSA interview questions
  • Add Major ML topics
    • Linear Regression
    • Logistic Regression
    • SVM
    • Random Forest
    • Gradient boosting
    • PCA
    • Collaborative Filtering
    • K-means clustering
    • kNN
    • ARIMA
    • Neural Networks
    • Decision Trees
    • Overfitting, Underfitting
    • Unbalanced, Skewed data
    • Activation functions relu/ leaky relu
    • Normalization
    • DBSCAN
    • Normal Distribution
    • Precision, Recall
    • Loss Function MAE, RMSE
  • Add Pandas questions
  • Add NumPy questions
  • Add TensorFlow questions
  • Add PyTorch questions
  • Add list of learning resources

Report/Solve Issues

Issues

🔧 To report any issues find me on LinkedIn or raise an issue on GitHub.

🛠 You can also solve existing issues on GitHub and create a pull request.

Say Thanks

😊 If this platform helped you in any way, it would be great if you could share it with others.

\ No newline at end of file +

Useful Commands

Useful Documents

FAQ

Credits

Maintained by

👨‍🎓 Kuldeep Singh Sidhu

Github: github/singhsidhukuldeep https://github.com/singhsidhukuldeep

Website: Kuldeep Singh Sidhu (Website) http://kuldeepsinghsidhu.com

LinkedIn: Kuldeep Singh Sidhu (LinkedIn) https://www.linkedin.com/in/singhsidhukuldeep/

Contributors

😎 The full list of all the contributors is available here

Current Status

Maintenance Website shields.io GitHub pages status GitHub up-time BOT Commits

Issues Total Commits Contributors Forks Stars Watchers Branches

License: AGPL v3 made-with-python made-with-Markdown repo- size Followers

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Skip to content

Projects

Introduction

These are the projects that you can take inspiration from and try to improve on them. ✍️

Number of Projects

Github Google Collab

List of projects

Natural Language processing (NLP)

Title Description Source Author
Text Classification with Facebook fasttext Building the User Review Model with fastText (Text Classification) with response time of less than one second Github Kuldeep Singh Sidhu
Chat-bot using ChatterBot ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. Github Kuldeep Singh Sidhu
Text Summarizer Comparing state of the art models for text summary generation Github Google Collab Kuldeep Singh Sidhu
NLP with Spacy Building NLP pipeline using Spacy Github Kuldeep Singh Sidhu

Recommendation Engine

Title Description Source Author
Recommendation Engine with Surprise Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering Github Google Collab Kuldeep Singh Sidhu

Image Processing

Title Description Source Author
Facial Landmarks Using Dlib, a library capable of giving you 68 points (land marks) of the face. Github Kuldeep Singh Sidhu

Reinforcement Learning

Title Description Source Author
Google Dopamine Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. Github Google Collab Kuldeep Singh Sidhu
Tic Tac Toe Training a computer to play Tic Tac Toe using reinforcement learning algorithms. Github Google Collab Kuldeep Singh Sidhu

Others

Title Description Source Author
TensorFlow Eager Execution Eager Execution (EE) enables you to run operations immediately. Github Google Collab Kuldeep Singh Sidhu
\ No newline at end of file + Projects - Data Science Interview preparation
Skip to content

Projects

Introduction

These are the projects that you can take inspiration from and try to improve on them. ✍️

Number of Projects

Github Google Collab

List of projects

Natural Language processing (NLP)

Title Description Source Author
Text Classification with Facebook fasttext Building the User Review Model with fastText (Text Classification) with response time of less than one second Github Kuldeep Singh Sidhu
Chat-bot using ChatterBot ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. Github Kuldeep Singh Sidhu
Text Summarizer Comparing state of the art models for text summary generation Github Google Collab Kuldeep Singh Sidhu
NLP with Spacy Building NLP pipeline using Spacy Github Kuldeep Singh Sidhu

Recommendation Engine

Title Description Source Author
Recommendation Engine with Surprise Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering Github Google Collab Kuldeep Singh Sidhu

Image Processing

Title Description Source Author
Facial Landmarks Using Dlib, a library capable of giving you 68 points (land marks) of the face. Github Kuldeep Singh Sidhu

Reinforcement Learning

Title Description Source Author
Google Dopamine Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. Github Google Collab Kuldeep Singh Sidhu
Tic Tac Toe Training a computer to play Tic Tac Toe using reinforcement learning algorithms. Github Google Collab Kuldeep Singh Sidhu

Others

Title Description Source Author
TensorFlow Eager Execution Eager Execution (EE) enables you to run operations immediately. Github Google Collab Kuldeep Singh Sidhu
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