Machine Learning

Machine Learning involves ability to process enormous amount of data to learn to predict an outcome, and improves its ability with experience. In a way it is complicated statistical modelling. But since the data is large, there is advantage of using the big data analysis and deep learning. The crucial factor is in enabling the unstructured data such as relevant images, speech etc to feed into structured business decision-making process. Critical importance needs to be given to

Data Experts who can program the data and provide results
Business Translators who can interpret the results for the Senior Management by providing guidance about what the results mean

AI Mitra can help in ensuring that the guts of the learning systems is built using the in house business experts and the models can ensure robust checks that ensure debiasing to provide the best solutions. We can also ensure that in the long run such business experts get trained in the necessary systems to ensure that they continue to play the stakeholder role that they originally have.

Why is machine learning important?

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

What are the different types of machine learning?

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

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GET IN TOUCHHow Machine Learning Works

UC Berkeley breaks out the learning system of a machine learning algorithm into three main parts.

A Decision Process

In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.

An Error Function

An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.

A Model Optimization Process

If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.

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