Author(s): Pratik Shukla, Roberto Iriondo
Data science and machine learning are scientific disciplines that are ruled by programming and mathematics. Nowadays, most corporations globally generate immense amounts of data that can be further analyzed and visualized by experts to understand trends and forecast predictions. For instance, we can only perform accurate data visualization if our data is clear and understandable.
However, organizations’ data is (frequently) too messy to tinker with — therefore, finding structures and important patterns in data is a crucial task for data science. Statistics provides the methods and tools to find hidden structures and patterns in data…
Author(s): Roberto Iriondo
Data labeling is an essential part of the machine learning workflow, particularly data preprocessing, where both input and output data are labeled for classification to present a learning base for planned data processing.
We use data labeling to identify raw data, such as objects in images, videos, text, and so on. It works by affixing one or more significant and informative labels to produce context so that a model can learn from it . …
Many industry-leading companies are already using data science to address better decision-making and to improve their marketing analytics. With the expanded industry data, greater availability of resources, lower storage, and processing costs, an organization can now process large volumes of frequent, and granular data with the help of several data science techniques and obtain the leverage needed to create composite models, deliver crucial decision-making, and obtain essential consumer acumen with higher accuracy than ever before.
Using data science principles in marketing analytics is a determined, cost-effective, practical way for many companies to observe a customer’s…
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In data science, machine learning, and other quantitative data fields, it is important to enhance your data structure concepts. Swapping variables becomes a crucial step whenever we are working with a model that requires swapping specific values. This tutorial will dive in on how we can trade two variables in Python using five straightforward and applicable methods.
In this program, we will use the…
join( ) function of the pandas' library is used to join columns of another DataFrame. It can efficiently join columns with another DataFrame on index or on a key column. We can also join multiple DataFrame objects by passing a list. Let’s start by understanding its’ syntax and parameters. The companion materials for this tutorial can be found under our resources section.
The most crucial and time-consuming part of any data science project is data cleansing and preparation. Thankfully, there are many powerful tools available that help us expedite this process.
The pandas’ library is one of the widely used data analysis libraries in python. Before using our models to perform data analysis on our data, it is critical to find any missing values that may affect our outputs.
Missing data occurs when a user being surveyed does not share their data. …
This tutorial will dive deeper into Pandas’
pd.melt() function to understand its core functionalities with graphics and its implementation in Python. We will first see the syntax and parameters for this method. Then we will take a few examples to understand all the
pd.melt() function parameters. The companion resources to this tutorial can be found either on Google Colab or Github.
The Pandas melt() function is within many other methods used to reshape the pandas DataFrames from wide to a long format which is particularly useful in data science. However, the
pd.melt() function is the…
Author(s): Buse Yaren Tekin
Recently, object detection has continued to evolve from its current state, and due to its technology, it can be found across almost every technological platform. Whether it is through image classification, recognition, or localization, these are all based on object detection.
Convolutional neural networks (CNNs) can bring together many object recognition and classification techniques together by incorporating deep learning and computer vision methods. In computer vision, convolutional neural networks, as the name suggests, apply a convolution layer in each pixel image in a dataset.
Due to computer vision and deep learning fundamentals in its primary structure…
This article covers an extensive introduction with step-by-step explanations and code on data pipelines to introduce the foundations of data engineering. Data pipelines are used extensively in data science and machine learning and are crucial on machine learning workflows to integrate data from multiple streams to gain business intelligence for competitive analysis and advantage.
A data pipeline is a set of rules that stimulates and transforms data from multiple sources to a destination where new values can be obtained. In the most simplistic form, pipelines may extract only data from different sources such as a…
SVM stands for support vector machine, and although it can solve both classification and regression problems, it is mainly used for classification problems in machine learning (ML). SVM models help us classify new data points based on previously classified similar data, making it is a supervised machine learning technique. The companion resources to this article can be found either on Google Colab or Github.
Classification is a supervised ML task that requires machine learning algorithms that learn how to assign a class label to examples from a problem domain. …