The first step in any EDA process is to load the image data into your Integrated Development Environment (IDE) workspace, such as VS Code, Jupyter Notebook, or any other Python …
Loading the data – Exploring Image Data Read MoreIn this section, we explore the power of visualization tools and techniques to gain meaningful insights into the characteristics and patterns of image data. Using Python libraries such as Matplotlib …
Visualizing image data using Matplotlib in Python – Exploring Image Data Read MoreIn this chapter, we will learn how to explore image data using various packages and libraries in Python. We will also see how to visualize images using Matplotlib and analyze …
Technical requirements – Exploring Image Data Read MoreIn summary, the line cluster_mask = (kmeans_model.labels_ == cluster_idx) creates a mask that helps you filter and select the data points in a specific cluster based on their assigned cluster …
Using k-means clustering to label regression data – Labeling Data for Regression-2 Read MoreUsing semi-supervised learning to label regression data In this section, we are going to use semi-supervised learning to label the regression data. Semi-supervised learning is a type of machine learning …
Pseudo-labeling – Labeling Data for Regression-1 Read MoreIn this section, we are going to generate house price labels using summary statistics of a small set of available labeled housing price data. This is useful in real-world projects …
Using summary statistics to generate housing price labels – Labeling Data for Regression Read MoreHere, pairwise_distances from scikit-learn is used to calculate the distances between cluster centers (inter-cluster distances). The result is a matrix where the element at position (i, j) represents the distance …
What is unsupervised learning? – Labeling Data for Classification-2 Read MoreUnsupervised learning is a category of machine learning where the algorithm is tasked with discovering patterns, structures, or relationships within a dataset without explicit guidance or labeled outputs. In other …
What is unsupervised learning? – Labeling Data for Classification-1 Read MoreL_train is passed to the label model to predict the labels, as follows:X[‘Labels’] = label_model.predict(L=L_train) We get the following result: Figure 2.4 – Probabilistic labels As we have seen, we …
Predicting labels – Labeling Data for Classification Read MoreLabeling rules The labeling rules from our small-volume income dataset are as follows: Constants Let us define various constants that are used in our labeling functions as follows. Abstain indicates …
Labeling functions – Labeling Data for Classification Read More