Data labelling is an essential aspect of data preprocessing for machine learning, mainly supervised learning. It provides a foundation for future data processing, and supervised learning utilizes input and output data tagged for categorization.

Most practical machine learning models use supervised learning, which maps a single input to a single output using an algorithm. For supervised learning to work, you need a set of labelled data that the model can use to learn how to make good decisions. So, in machine learning, the term "ground truth" is often used to describe an adequately labelled dataset that is used as an objective standard to test and train a model. The accuracy of the trained model depends on how accurate the ground truth is. Because of this, it is essential to spend the right amount of time and money to ensure that the data labels are accurate.

In this regard, we have compiled a list of exciting data labelling courses:

Develop Custom Object Detection Models with NVIDIA and Azure Machine Learning - Microsoft

Azure Machine Learning studio is a graphical user interface-based integrated development environment for building and deploying Machine Learning workflow on Azure. Using this service and NVIDIA GPU-accelerated virtual machines, see how to create bespoke object detection models.

This course includes:

• Azure Machine Learning Studio is a GUI-based Integrated Development Environment (IDE) for building and deploying Machine Learning workflow on Azure.
• Learn how to create custom object detection models using this service and GPU-accelerated virtual machines from NVIDIA.
• It will teach you how to set up resources for launching Azure Machine Learning.
• Learn how to design, manage, and monitor data labelling projects using Azure Machine Learning data labelling
• Learn how to use AutoML to train a labelled dataset and create an object detection model for production.

Machine Learning Data Lifecycle in Production - Coursera

In this course of Machine Learning Engineering for Production Specialization:

• You will construct data pipelines, 
• Implement feature engineering, transformation, and selection, and 
• Establish the data lifecycle and data evolution with enterprise data schemas. 

In addition, understanding machine learning and deep learning ideas are vital, but production engineering skills are also required to develop a successful AI profession. Machine learning engineering for production blends the fundamentals of machine learning to build production-ready skills.

Optimize ML Models and Deploy Human-in-the-Loop Pipelines by DeepLearning.ai and AWS

One will learn several performance-improving and cost-reduction strategies as part of the Practical Data Science Specialization, including comparing prediction performance, generating new training data using human intelligence, and automatically tuning model accuracy. Additionally, using Amazon Augmented AI and Amazon SageMaker Ground Truth, one may set up a pipeline with a person in the loop to correct misclassified predictions and produce new training data. Practical data science is designed to handle enormous datasets that cannot be accommodated by local technology and may come from several sources.

Designing Human-Centred AI Products and Services

This six-week course from Hanyang University will cover human-computer interaction (HCI) and how it may be utilized to make AI beneficial for all individuals. In addition, you will study how the HCI field can address the shortcomings of contemporary AI and how to reinvent it as human-centered AI to add value to every individual's life. You will gain an understanding of human-computer interaction to comprehend why HCI should be integrated with AI. Furthermore, you will investigate how the classic HCI approach has been altered for the new world of AI, redefining how we interact with products and services.