Decision trees in machine learning are a common way of representing the decision-making process through a branching, tree-like structure. It’s often used to plan and plot business and operational decisions as a visual flowchart. The approach sees a branching of decisions which end at outcomes, resulting in a tree-like structure or visualization. Decision trees […]
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Machine Learning Regression is a technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It’s used as a method for predictive modeling in machine learning, in which an algorithm is used to predict continuous outcomes. Solving regression problems is one of the most common applications for machine learning
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Covariate shift is a specific type of dataset shift often encountered in machine learning. It is when the distribution of input data shifts between the training environment and live environment. Although the input distribution may change, the output distribution or labels remain the same. Covariate shift is also known as covariate drift, and is a very
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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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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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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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