Training Datasets for Machine Learning Projects: A Guide

Here is a practical guide to training datasets for machine learning projects

Machine learning is the process of creating systems that can learn from data and make predictions or decisions based on it. ML has many applications in various fields, such as computer vision, NLP, speech recognition, recommendation systems, and more. However, to build a successful machine-learning project, you will need data.

Data is the fuel that powers machine-learning models. Without data, machine learning models cannot learn anything, and without quality data, machine learning models cannot perform well. Therefore, finding, collecting, and preparing data is a crucial step in any machine-learning project. In this article, we will guide you through the process of getting training datasets for ML projects, and provide you with some useful resources and tips along the way.

What Is a Training Dataset?

A training dataset is a collection of data that is used to train a machine-learning model. A training dataset typically consists of two parts: features and labels. Features are the input data that describe the characteristics of the data points, such as images, text, numbers, etc. Labels are the output data that indicate the desired outcome or category of the data points, such as classes, scores, ratings, etc.

Depending on the type of machine learning problem, the labels may or may not be available. In supervised learning, the labels are provided along with the features, and the goal is to learn a function that maps the features to the labels. In unsupervised learning, the labels are not provided, and the goal is to discover patterns or structures in the features. In semi-supervised learning, some labels are provided, and some are not, and the goal is to leverage both labelled and unlabeled data to improve learning performance.

Where To Find Training Datasets?

There are many sources and methods to obtain training datasets for machine learning projects. Some of the common ones are:

Open dataset aggregators: These are platforms or websites that host and provide access to a large number of publicly available datasets for various domains and purposes. Some examples of open dataset aggregators are Kaggle, Google Dataset Search, and UCI Machine Learning Repository. These aggregators allow you to browse, download, and explore datasets from different sources and formats and often provide useful metadata, documentation, and analysis tools.

Public government datasets: These are datasets that are collected and published by government agencies or organizations for public use and benefit. Some examples of public government datasets are Data.gov, EU Open Data Portal, World Bank Open Data, and UNdata. These datasets cover a wide range of topics and sectors, such as health, education, environment, economy, and more, and often provide high-quality and reliable data.

Machine learning datasets for specific domains: These are datasets that are curated and designed for specific machine learning tasks or challenges, such as image classification, natural language processing, speech recognition, etc. Some examples of machine learning datasets for specific domains are ImageNet, MNIST, COCO, SQuAD, and LibriSpeech. These datasets are often used as benchmarks or standards to evaluate and compare the performance of different machine-learning models and algorithms.

Web scraping and crawling: These are techniques that involve extracting and collecting data from web pages or websites, using automated scripts or programs. Web scraping and crawling can be useful to obtain data that is not readily available or accessible in other formats, such as tables, charts, text, images, etc. However, web scraping and crawling also require some technical skills and ethical considerations, such as respecting the terms and conditions, privacy policies, and robots.txt files of the websites, and avoiding excessive requests or bandwidth consumption.

Data generation and augmentation: These are techniques that involve creating or modifying data, using synthetic methods, such as randomization, interpolation, transformation, etc. Data generation and augmentation can be useful to increase the size, diversity, and quality of the data, especially when the original data is scarce, imbalanced, noisy, or incomplete. However, data generation and augmentation also require some domain knowledge and validation, to ensure that the generated or augmented data is realistic, relevant, and consistent with the original data.

How to prepare training datasets?

Once you have obtained your training dataset, you need to prepare it for your machine-learning project. This involves performing some data preprocessing and data analysis steps, such as:

Data cleaning: This is the process of removing or correcting errors, inconsistencies, outliers, duplicates, missing values, or irrelevant data from the dataset, to improve its quality and accuracy.

Data transformation: This is the process of converting or modifying the data into a suitable format or representation for the machine learning model, such as scaling, normalizing, encoding, binning, etc.

Data exploration: This is the process of examining and understanding the data, using descriptive statistics, visualizations, correlations, distributions, etc., to gain insights and identify patterns, trends, or anomalies in the data.

Data splitting: This is the process of dividing the data into two or more subsets, such as training, validation, and test sets, to train, tune, and evaluate the machine learning model, and avoid overfitting or underfitting.

Conclusion

Training datasets are essential for machine learning projects, as they provide the data that the machine learning models learn from and make predictions or decisions based on.