Can a machine do it? If so, how does a machine do it?

In my previous post (Part-1), I have discussed the overview, the importance of natural language processing and the 9 steps (Fig.1) required for a machine to understand human language. Let’s summarize and explain each of them before deep dive.

· Tokenization: Tokenization is a technique of chopping a sequence of input text into words, symbols, phrases or text elements. It is known as a token. For example, if the user input is “what is the monthly premium?”, there will be six tokens after the tokenization process. These are: “what”, “is”, “the”, “monthly”, “premium”, “?”.

Data scientists have career options and won’t abide by bad managers for very long.

A successful project does not happen by accident. Most of the data science projects fail due to improper workflow, the manager’s attitude and the lake of skills. The truth is that being successful in data science in a business is very challenging. The outcome of data science projects, software development or IT projects is very different from each other. Managing them as they are alike a recipe for heartache.

Comparing data science to IT project is not comparing apple to apple.

Many project/product managers have incorrect assumptions about what data science is and limited understanding of how to support it…

Can a machine do it? If so, how does a machine do it?

Poster presented at ACM Turing Conference [Ref. 1], May 2019, China

Human language is highly ambiguous … It is also ever-changing and evolving. People are great at producing language and understanding language and are capable of expressing, perceiving, and interpreting very elaborate. In theory, we can understand and even predict human behaviour using that information. Can a machine do it? If so, how does a machine do it?

Yes, a machine can do it.

Great progress on AI made it easier for a data scientist. Understanding human or natural language is part of computational linguistics known as natural language processing (NLP). Not only understanding languages but also language translation, question-answering, text…

As a data scientist, I was involved in a data analytics team to build predictive models for employee attrition and performance. It was a binary classification where we classified employees (object) as belongs to a target class A or class B. We have a training dataset for each client for each class of object and utilized a binary classification algorithm to predict the class to which a new object belongs. So far so good, but how did we solve problems in which our training dataset only contains one class, and the rest are objects of an unknown class?

The scenario…

It’s hard to know where to start or to dive into the fascinating world of data and AI. The first time if you work on a data science project, there’s no clear vision, a sense of an unclear pathway in regards to the necessary steps of what it takes to do a complete analysis and to complete the data science project.

In ELMO, we have the following systematic and structured approach for data scientists that help to maximize chances of success in a data science project at the lowest cost. To succeed, the eight major steps (Fig.1) …

Mohammad Nuruzzaman

Data Scientist at ELMO… Deliver High-impact AI solutions through MLOps & Predictive Analytics.

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