Quiz Explaination
Supervised Learning: All data is labeled and the algorithms learn to predict the output from the
input data
Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from
the input data.
Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of
supervised and unsupervised techniques can be used to solve problem.
Unsupervised learning problems can be further grouped into clustering and association problems.
Clustering: A clustering problem is where you want to discover the inherent groupings
in the data, such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy A also tend to buy B.
Supervised Learning: All data is labeled and the algorithms learn to predict the output from the
input data
Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from
the input data.
Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of
supervised and unsupervised techniques can be used to solve problem.
Unsupervised learning problems can be further grouped into clustering and association problems.
Clustering: A clustering problem is where you want to discover the inherent groupings
in the data, such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy A also tend to buy B.
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What is feature selection? Why do we need it?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.
What are the decision trees?
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy.
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy.
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What are the benefits of a single decision tree compared to more complex models?
easy to implement
fast training
fast inference
good explainability
easy to implement
fast training
fast inference
good explainability
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🤓 Technical Python concepts tested in the data science job interviews are:
- Data types.
- Built-in data structures.
- User-defined data structures.
- Built-in functions.
- Loops and conditionals.
- External libraries (Pandas).
Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html
- Data types.
- Built-in data structures.
- User-defined data structures.
- Built-in functions.
- Loops and conditionals.
- External libraries (Pandas).
Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html
Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
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Some useful Machine Learning projects for practice
https://elitedatascience.com/machine-learning-projects-for-beginners
https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f
https://www.springboard.com/blog/machine-learning-projects
https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional
https://elitedatascience.com/machine-learning-projects-for-beginners
https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f
https://www.springboard.com/blog/machine-learning-projects
https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional
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What are precision, recall, and F1-score?
Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).
Where TP is true positives, FP is false positives and FN is false negatives
In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.
F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.
Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).
Where TP is true positives, FP is false positives and FN is false negatives
In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.
F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.
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What is unsupervised learning?
Unsupervised learning aims to detect patterns in the data where no labels are given.
Unsupervised learning aims to detect patterns in the data where no labels are given.
Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words?
Usually logistic regression is better because bag of words creates a matrix with large number of columns. For a huge number of columns logistic regression is usually faster than gradient boosting trees.
Usually logistic regression is better because bag of words creates a matrix with large number of columns. For a huge number of columns logistic regression is usually faster than gradient boosting trees.
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What is clustering? When do we need it?
Clustering algorithms group objects such that similar feature points are put into the same groups (clusters) and dissimilar feature points are put into different clusters.
Clustering algorithms group objects such that similar feature points are put into the same groups (clusters) and dissimilar feature points are put into different clusters.
What is bag of words? How we can use it for text classification?
Bag of Words is a representation of text that describes the occurrence of words within a document. The order or structure of the words is not considered. For text classification, we look at the histogram of the words within the text and consider each word count as a feature.
Bag of Words is a representation of text that describes the occurrence of words within a document. The order or structure of the words is not considered. For text classification, we look at the histogram of the words within the text and consider each word count as a feature.
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Source codes for data science projects 👇👇
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍
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Free Data Science courses from Udemy and Udacity
👇👇
Intro to Data Science
https://imp.i115008.net/rn2beD
Data Analysis and Visualization
https://imp.i115008.net/JrBjZR
Data Analysis with R by Facebook
https://imp.i115008.net/gbJr5r
Introduction to Data Science using Python
https://ern.li/OP/1qvkxbfaxqj
Intro to Data for Data Science
https://ern.li/OP/1qvkxbfbmf8
Data Science with Analogies, Algorithms and Solved Problems
https://ern.li/OP/1qvkxbfcehz
Introduction to Data Science for Complete Beginners
https://bit.ly/3sh4oPO
ENJOY LEARNING 👍👍
👇👇
Intro to Data Science
https://imp.i115008.net/rn2beD
Data Analysis and Visualization
https://imp.i115008.net/JrBjZR
Data Analysis with R by Facebook
https://imp.i115008.net/gbJr5r
Introduction to Data Science using Python
https://ern.li/OP/1qvkxbfaxqj
Intro to Data for Data Science
https://ern.li/OP/1qvkxbfbmf8
Data Science with Analogies, Algorithms and Solved Problems
https://ern.li/OP/1qvkxbfcehz
Introduction to Data Science for Complete Beginners
https://bit.ly/3sh4oPO
ENJOY LEARNING 👍👍
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Data Science & Machine Learning pinned «Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR…»
Forwarded from Jobs | Internships | Placement | Interviews
Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
Fake_News_Detection_Machine_learning_project.rar
8.3 MB
Fake news Detection Machine Learning Project with 92%Accuracy
it contain compressed file in which "jupyter notebook file and dataset"✅
it contain compressed file in which "jupyter notebook file and dataset"✅
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dice_roll.py
445 B
🎲Dice_roll_Simulator_Gui with python in 2 minute 😊