In this article, we are going to see how to create a word cloud in python. A word cloud creates a collage of most prominent words from the given text. The size of each word is […]
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KERAS Callbacks
Keras comes with a long list of predefined callbacks that are ready to use. Keras callbacks are functions that are executed during the training process. According to Keras Documentation, A callback is a set of functions to be […]
Continue readingMore TagTypes of Data
It is necessary to understand the different types of data we are dealing with to choose the right visualization technique or statistical measure for our data. In this blog post, we’ll discuss two types of data […]
Continue readingMore TagIntroduction to Hyper-parameter Tuning: GridSearchCV and RandomSearchCV
Most of the machine learning algorithms contain a number of hyperparameters that we can tune to improve the model’s performance. The hyperparameter controls the behaviors of the algorithm and has an enormous impact on the results. […]
Continue readingMore TagMeasure of Central Tendency and Measure of Spread
Summarizing the quantitative data can help us understand them better. In this article, we’ll see various methods to summarize quantitative data by the measure of central tendency(such as mean, median, and mode) and by the measure […]
Continue readingMore TagWhy High Dimensional Data are a Curse?
In the era of big data, massive datasets(not only in the number of samples being collected but also in the number of features) are increasingly prevalent in many disciplines and are often difficult to interpret. In […]
Continue readingMore TagConverting Raw Text to Numerical Vectors using Bag of Words, N_Grams and TF-IDF
If we are dealing with text documents and want to perform machine learning on text, we can’t directly work with raw text. We first need to convert the text into numbers or vectors of numbers. In this article, […]
Continue readingMore TagIntroduction to Logistic Regression
In this article, we’ll discuss a supervised machine learning algorithm logistic regression. Logistic regression is a probabilistic classifier. It outputs the probability of a point belonging to a specific class. The output lies between [0,1]. Logistic […]
Continue readingMore TagAssumptions Made by Ordinary Least Squares(OLS)
Introduction: Ordinary Least Squares(OLS) is a commonly used technique for linear regression analysis. OLS makes certain assumptions about the data like linearity, no multicollinearity, no autocorrelation, homoscedasticity, normal distribution of errors. Violating these assumptions may reduce […]
Continue readingMore TagIntroduction to K-Nearest Neighbors
The K-nearest neighbor(K-NN) classifier is one of the easiest classification methods to understand and is one of the most basic classification models available. K-NN is a non-parametric method which classifies based on the distance to the […]
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