Python Modules

In the previous post, we reviewed some of the basic data-cleaning functions available in PyJanitor. This post aims to understand some of the miscellaneous functions offered by the data-cleaning clean API. For starters, PyJanitor is a data cleaning and processing API inspired by the R package’s Janitor and built on top of the Pandas library

The ‘self’ is a keyword used in object-oriented programming to refer to the instance of a class. It acts as a placeholder for the instance itself, providing access to the attributes and methods defined within the class. When a method is called on an instance, ‘self’ allows the method to access and manipulate the instance’s

The applications of machine learning are growing rapidly and undoubtedly, are making business, education, and marketing easier and hassle-free. One such application of machine learning is anomaly detection. Anomaly detection is the process of identifying unusual patterns or behaviors in data that deviate significantly from the norm. It has wide-ranging applications, from detecting fraudulent transactions

The integration of R with the TensorFlow package along with Keras has allowed developers to utilize TensorFlow’s functionalities with R. Deep learning has become easier for R users due to the integration of R Keras with Python’s tensorflow module. However, this doesn’t mean that errors during installation have become null. As with every process, chances

PyTorch is a popular Python library that helps all deep learning enthusiasts. The torch.nn module is a very important component of PyTorch which helps with the building and training of neural networks. In this article, we will take a deep dive into the torch.nn module, its key components, and the implementation of the module in

Pytorch is a prevalent machine learning library in Python programming language. Pytorch is a handy tool in neural networks and torch.optim module is used in various neural network models for training. This module provides us with multiple optimization algorithms for training neural networks. In this article, we will understand in depth about the torch.optim module

Machine Learning is a diverse field growing continuously with applications spread across many domains like agriculture, finance, marketing, and many more. To utilize machine learning in any domain, we need first to build a model suitable for the use case. Be it classification, regression, or anomaly detection. Refer to this article on regression vs classification

Imagine you are working on your dream machine-learning project in Python and collecting huge data from various sources. How do we integrate this huge data? Use data frames! A data frame is a table-like data storage format that stores data in the form of rows and columns. One problem is solved. However, since the data