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Detecting Outliers with Z-scores in Python

#artificialintelligence

Z-score is a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean. In this video we will use z-score to find out outliers in our dataset. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: Blog - http://bhattbhavesh91.github.io


Derivative of the Sigmoid Activation function Deep Learning

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In this video, I will show you a step by step guide on how you can compute the derivative of a Sigmoid Function. Sigmoid function is a widely used activation function Deep Learning & Machine Learning. If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.


Git Tutorial for Beginners

#artificialintelligence

Git is the most popular version control system. In this Git tutorial, I'll show you what exactly Git is & also walk through its important commands such as add, commit, status, push and more. This tutorial is aimed for beginners. If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.


How to Perform Feature Selection with Categorical Data

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Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature selection is often straightforward when working with real-valued data, such as using the Pearson's correlation coefficient, but can be challenging when working with categorical data. The two most commonly used feature selection methods for categorical input data when the target variable is also categorical (e.g. In this tutorial, you will discover how to perform feature selection with categorical input data. How to Perform Feature Selection with Categorical Data Photo by Phil Dolby, some rights reserved.


Overview of feature selection methods

#artificialintelligence

Selecting the right set of features to be used for data modelling has been shown to improve the performance of supervised and unsupervised learning, to reduce computational costs such as training time or required resources, in the case of high-dimensional input data to mitigate the curse of dimensionality. Computing and using feature importance scores is also an important step towards model interpret-ability. This post shares the overview of supervised and unsupervised methods for performing feature selection I have acquired after researching the topic for a few days. For all depicted methods I also provide references to open-source python implementations I used in order to allow you to quickly test out the presented algorithms. However, this research domain is very abundant in terms of methods which have been proposed during the last 2 decades and as such this post only attempts to present my current limited view without any pretense for completeness.