This article is an attempt to check under what condition we can go for a Z -Test or a T-Test. We will further implement these tests in python.
The vocabulary helps in pre-processing of corpus text which acts as a classification and also a storage location for the processed corpus text. Once a text has been processed, any relevant metadata can be collected and stored.In this article, we will discuss the implementation of vocabulary builder in python for storing processed text data that can be used in future for NLP tasks.
In the real world, the size of datasets is huge which comes as a challenge for every data science programmer. Working on it takes a lot of time, so there is a need for a technique that can increase the algorithm’s speed. Most of us are familiar with the term parallelization that allows for the distribution of work across all available CPU cores. Python offers two built-in libraries for this process, multiprocessing and multithreading.
XGBoost is developed on the framework of Gradient Boosting.
In real-world, training and model prediction is one phase of the machine learning life-cycle. But it won’t be helpful to anyone other than the developer as no one will understand it. So, we need to create a frontend graphical tool that users can see on their machine. The easiest way of doing it is by deploying the model using Flask.
In this article, we will discuss how to use flask for the development of our web applications. Further, we will deploy the model on google platform environment.
In this era, Short message service or SMS is considered one of the most powerful means of communication. As the dependence on mobile devices has drastically increased over the period of time it has led to an increased number of attacks in the form of SMS Spam.The main aim of this article is to understand how to build an SMS spam detection model. We will build a binary classification model to detect whether a text message is spam or not.
In this article, we will be discussing the key techniques that can be used to choose the right machine algorithm in a particular work. Through this article, we will discuss how we can decide to use which machine learning model using the plotting of dataset properties.
The main aim of this article is to discuss the methods for checking the stationarity in time series data. We will do the experiments on the time series data to check this.
This article demonstrates how we can implement a deep learning model with ShuffleNet architecture to classify images of CIFAR-10 dataset. Here, we define a Convolutional Neural Network (CNN) model using Torch to train this model. We will test the model to check the reduction in computational cost and obtain accuracy.
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