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Postlar arxiv
9 Best Machine Learning Use cases in our Daily Lives 🚀
👓 Youtube Recommendation
👓 Voice Assistants
👓 arrow Smartphone Camera
👓 Google Maps routes
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Introduction_to_Machine_Learning_with_Python_A_Guide_for_Beginners.epub1.95 MB
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TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?
Answer: This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.
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Jeroen_Janssens_Data_Science_at_the_Command_Line_Obtain,_Scrub,.epub7.96 MB
Core machine learning concepts explained through memes and simple charts created by Mihail Eric.
Natural_Language_Processing_with_Python_by_Steven_Bird,_Ewan_Klein.pdf5.18 MB
Machine-Learning-With-Python-For-Everyone-Pearson-2020.pdf9.00 MB
DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS
1. What is a logistic function? What is the range of values of a logistic function?
f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.
2. What is the difference between R square and adjusted R square?
R square and adjusted R square values are used for model validation in case of linear regression. R square indicates the variation of all the independent variables on the dependent variable. i.e. it considers all the independent variable to explain the variation. In the case of Adjusted R squared, it considers only significant variables(P values less than 0.05) to indicate the percentage of variation in the model.
Thus Adjusted R2 is always lesser then R2.
3. What is stratify in Train_test_split?
Stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset. So if my input data has 60% 0's and 40% 1's as my class label, then my train and test dataset will also have the similar proportions.
4. What is Backpropagation in Artificial Neuron Network?
Backpropagation is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization.
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Thinking_in_Pandas_How_to_Use_the_Python_Data_Analysis_Library_the.pdf2.31 MB
How is kNN different from k-means clustering?
kNN, or k-nearest neighbors is a classification algorithm, where the k is an integer describing the number of neighboring data points that influence the classification of a given observation. K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data. Both accomplish different tasks.
