Data Labeling in Machine Learning with Python

Good machine-learning models depend on useful labeled data, but building that dataset can be a substantial task. Data Labeling in Machine Learning with Python focuses on ways to explore and annotate data programmatically, with examples spanning tables, images, text, audio, and video.

Vijaya Kumar Suda takes the subject from exploratory analysis into practical labeling methods, connecting the characteristics of a dataset with the choices involved in preparing it for model training.

Start with the data, not just the labels

The opening chapters cover exploratory data analysis for tabular data, including summaries, visualizations, profiling, and the use of tools such as Pandas, Seaborn, and ydata-profiling. That foundation leads into classification and regression examples, where labeling approaches include rules, Snorkel, semi-supervised learning, K-means clustering, and data augmentation.

Methods for more than spreadsheets

The book also explores image labeling through rules, transformations, and transfer learning, then broadens its scope to text, audio, and video. Generative AI and large language models appear among the approaches to text labeling, alongside machine-learning techniques and annotation tools. The result is a tour of different data types and methods rather than a guide limited to one labeling workflow.

Python examples with practical context

Across its 17 chapters, the book introduces libraries and tools used in data exploration and annotation, including OpenAI-related techniques and platforms for managing labeling work. Examples touch on tasks such as classification and sentiment analysis, helping readers see how labeling methods relate to the data they are preparing.

Who may find it useful?

Machine-learning engineers, data scientists, data engineers, and Python developers will find material relevant to preparing datasets for model training. Readers who already know some Python may be especially comfortable following the technical examples.

For readers working through the often-overlooked step between raw data and model training, this book offers a broad, method-focused introduction to data labeling with Python.