First, what is machine learning (ML) and how can we define it?

Many definitions of machine learning float around. Machine learning is an application of artificial intelligence, that includes algorithms that parses the data, learn from the data, and then apply what they have learned to make informed decisions.

In terms of the place of machine learning and modern analytics, where does it fit into that very broad field of AI?

Organisations and scientists need to approach these questions by thinking about ML on a spectrum; from the simplest automation to the most sophisticated type of home automation.

How has machine learning developed over its long history and more recently, in particular?

In terms of evolution, the first notable place in the history of AI was Rosenblatt paper, about perceptron.

Moving to data, is there a need for more data-centric approaches to ML?

There is a very distinct shift from the field of data as well as machine learning. A merge is happening between traditional data warehouses, unstructured data and machine learning. Data-centric and model-centric machine learning are not separate things, says Rajdeep Biswas; these need to walk together. You can build heavy deep learning models. But to custom tune it, you still need data.

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The Data Analysis Bureau

The Data Analysis Bureau

We are a Data Science and Data Engineering Innovation Agency specialising in Machine Learning.