Cross validation is the use of various techniques to evaluate a machine learning model’s ability to generalize when processing new and unseen datasets. Generalization is a key aim of machine learning development as it directly impacts the model’s ability to function in a live environment. Cross validation is therefore a key step in ensuring a […]
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The concept of optimization is integral to machine learning. Most machine learning models use training data to learn the relationship between input and output data. The models can then be used to make predictions about trends or classify new input data. This training is a process of optimization, as each iteration aims to improve the
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Deep learning is a form of machine learning which aims to mimic and imitate the processes of the human brain through machine learning algorithms. The resulting models simulate how the human brain learns from its environment to detect, recognize and categorize objects and make nonlinear decisions. Deep learning models are also known as deep neural networks
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Outlier detection is a key consideration within the development and deployment of machine learning algorithms. Models are often developed and leveraged to perform outlier detection for different organizations that rely on large datasets to function. Economic modeling, financial forecasting, scientific research, and e-commerce campaigns are some of the varied areas that machine learning-driven outlier detection
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Concept drift is a major consideration for ensuring the long-term accuracy of machine learning algorithms. Concept drift is a specific type of model drift, and can be understood as changes in the relationship between the input and target output. The properties of the target variables may evolve and change over time. As the model has been
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Machine learning deployment is the process of deploying a machine learning model in a live environment. The model can be deployed across a range of different environments and will often be integrated with apps through an API. Learning how to deploy machine learning models is a key step in an organization gaining operational value from
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Transfer learning for machine learning is when elements of a pre-trained model are reused in a new machine learning model. If the two models are developed to perform similar tasks, then generalized knowledge can be shared between them. This approach to machine learning development reduces the resources and amount of labelled data required to train
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The four types of machine learning algorithms that we aim to explain are behind a range of technologies, whether providing predictive analytics to businesses or powering the decision-making of driverless cars. There are distinct approaches to machine learning which change how these systems learn from data. In general, there are four main types of machine
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Kubernetes is an open-source platform used to orchestrate and manage containers and containerized applications. It allows for the automation of key elements of container management, including scaling, scheduling, monitoring and container replication. It’s used by a huge range of organizations and developers to streamline the management of containerized workloads. Kubernetes was originally developed by Google
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Machine learning (ML) is a process by which a system learns from data to undergo iterative improvement without direct human control. Instead of operating on a static algorithm designed by a programmer, the algorithm is trained on sample data to create a model which makes sense of the data. Models can be used to provide
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