Data labelling is a process of recognising raw data (pictures, text files, videos, etc.) and adding one or more relevant and informative labels to deliver context so that a machine learning model may learn from it. For example, a label might indicate whether a given photo contains a cat or a bicycle, which words were uttered in an audio message, or if an x-ray of a person contains a tumour.
The majority of practical machine learning models today use supervised learning, which uses an algorithm to map a single input to a single output. To make supervised learning work, one will need a set of labelled data from which the model can learn to make the right decisions. So, in machine learning, a properly labelled dataset that one uses as the objective standard to assess and train a particular model is often termed as “ground truth.” The accuracy of the trained model depends on the accuracy of the ground truth; hence, spending the right amount of time and resources to ensure highly accurate data labelling is essential.
To that end, we have listed the top data labelling courses below:
About: The course with 11 instructors is available for free on the Coursera platform. This course is designed to teach learners efficient and scalable data labelling for machine learning and various business processes. The key approach adopted here is crowdsourcing which is based on splitting complex challenges into smaller tasks and then distributing them among a vast cloud of performers. One will get acquainted with crowdsourcing as a methodology in this course, thereby mastering various steps and techniques that ensure stable performance and quality. All these techniques will be implemented in practice straight away: throughout the course, the learner will be able to design their own crowdsourcing project.
The course is approximately 17 hours long, and one can earn a certificate on successful completion. All those with a general understanding of ML and AI can participate, and basic knowledge of HTML, JS, and CSS is an advantage.
Enrol here.
About: In line with the Machine Learning Engineering for Production Specialization, the course, available on Coursera, is designed to help build data pipelines by gathering, cleaning, and validating datasets and assessing data quality. The entire course is divided into four weeks:
The self-paced learning course can help you earn a certification upon completion. However, the course is suitable for advanced learners with some knowledge of AI or deep learning, intermediate level of Python skills, and experience with deep learning frameworks such as PyTorch, Keras, or TensorFlow.
Enrol here.
About: As part of the Practical Data Science Specialization, one will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. Additionally, one can set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth. Practical data science is geared towards handling massive datasets that do not fit in the local hardware and could originate from multiple sources.
With its availability on Coursera, the course is of 14 hours, self-paced and requires working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses as well.
Enrol here.
While working with crowd work platforms for datasets, it is essential to consider annotator subjectivity as it has the capability to make the data set of extremely high or low quality, which in turn affects the whole ML model.
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