Machine learning lets computers act like people by training them with data from the past and information about what might happen in the future. This section will examine some exciting machine learning algorithms like Augmented Analytics, Kernel principal component analysis, and Quickprop.

Augmented analytics

In 2017, Rita Sallam, Cindi Howson, and Carlie Idoine wrote a research paper for Gartner in which they used the term "augmented analytics" for the first time. It is a type of data analytics that uses machine learning and natural language processing to automate tasks that a specialist or data scientist would generally do. Furthermore, Business intelligence and analytics are the two main parts of augmented analytics. Various data sources are examined during the graph extraction step.

Defining Augmented Analytics:

• Machine learning is a method of computing that uses algorithms to find relationships, trends, and patterns in large amounts of data. It is a process that lets algorithms learn from data on the fly instead of having a set of rules they must follow.
• Natural Language Generation (NLG) is a feature of software that turns unstructured data into plain English that people can understand.
• Automating Insights means using machine learning algorithms to automate the data analysis process.
• Natural Language Query is a way for users to search for data by typing or speaking business terms into a search box.

Kernel principal component analysis

Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) that uses kernel methods. It is used in the field of multivariate statistics. The linear operations of PCA are done in a reproducing kernel Hilbert space with the help of a kernel. In addition, the PCA method is a linear one. That is, we can only use it with datasets we can separate in a straight line. It does a great job with datasets that can be divided linearly. But if we use it on nonlinear datasets, we might not get the best way to reduce the number of dimensions. Kernel PCA uses a kernel function to project a dataset into a higher-dimensional feature space where it can be separated linearly. It's kind of like how Support Vector Machines work.

Furthermore, the idea behind KPCA is based on the idea that many datasets that we can't separate linearly in their own space can be separated linearly in a higher-dimensional space. So the researchers did simple math operations on the original data dimensions to add the new dimensions.

Quickprop

Quickprop is an algorithm based on Newton's method that uses iterations to find the minimum of the loss function of an artificial neural network. The algorithm is sometimes put in the second-order learning methods group. It uses a quadratic approximation of the previous gradient step and the current gradient, which is expected to be close to the minimum of the loss function. This approach is based on the idea that the loss function is locally about the square and tries to describe it with an upwardly open parabola.

Furthermore, QuickProp is a second-order optimization algorithm that speeds up optimization by simply approximating the Hessian diagonal. This approach makes it a Quasi-Newton algorithm. So far, only standard neural network training has been studied and evaluated. But the current architectures of neural networks, like CNNs, have much larger parameters. Also, when backpropagation is used to figure out the gradients, the error gets more significant as the number of layers increases. To better understand how well it works, we test the algorithm in both simulated and real-world situations and compare it to a well-known optimization method called gradient descent (GD).