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Beginner's Guide to Exploratory Data Analysis on Text Data


There are no shortcuts in a machine learning project lifecycle. We can't simply skip to the model building stage after gathering the data. We need to plan our approach in a structured manner and the exploratory data analytics (EDA) stage plays a huge part in that. I can say this with the benefit of hindsight having personally gone through this situation plenty of times. In my early days in this field, I couldn't wait to dive into machine learning algorithms but that often left my end result hanging in the balance.

Introduction to NLP Techniques


Data Scientists work with tons of data, and many times that data includes natural languages like text and speech. That text is usually quite similar to the natural language that we use in our day-to-day life. In this blog, we are going to see some common NLP techniques, with the help of which we can begin performing analysis and building models from textual data. So, let's start with a formal definition… There are various use cases of NLP in our day-to-day life. Computers are great at working with structured data like spreadsheets and database tables, but the problem is we humans usually communicate in words, not in tables.

Natural Language Processing using Python


Natural Language Processing has always been a key tenet of Artificial Intelligence (AI). With the increase in the adoption of AI, systems to automate sophisticated tasks are being built. Some of these examples are described below. At the University of Tokyo's Institute of Medical Science, doctors used artificial intelligence to successfully diagnose a rare type of leukemia. The doctors used an AI system that cross-referenced the patient's genetic data with tens of millions of oncology papers and diagnosed cancer as rare secondary leukemia caused by myelodysplastic syndromes.

Complete Tutorial on Text Preprocessing in NLP


In any data science project life cycle, cleaning and preprocessing data is the most important performance aspect. Say if you are dealing with unstructured text data, which is complex among all the data, and you carried the same for modeling two things will happen. Either you come up with a big error, or your model will not perform as you expected. You might have wondered how the modern voice assistance system such as Google Assistance, Alexa, Siri can understand, process and respond to human language, so here comes the heavy lifter. Natural language processing, NLP, is a technique that comes from the semantic analysis of data with the help of computer science and artificial intelligence.

Natural Language Processing (NLP) with Python -- Tutorial


In this article, we explore the basics of natural language processing (NLP) with code examples. We dive into the natural language toolkit (NLTK) library to present how it can be useful for natural language processing related-tasks. Afterward, we will discuss the basics of other Natural Language Processing libraries and other essential methods for NLP, along with their respective coding sample implementations in Python. Computers and machines are great at working with tabular data or spreadsheets. However, as human beings generally communicate in words and sentences, not in the form of tables. Much information that humans speak or write is unstructured. So it is not very clear for computers to interpret such. In natural language processing (NLP), the goal is to make computers understand the unstructured text and retrieve meaningful pieces of information from it. Natural language Processing (NLP) is a subfield of artificial intelligence, in which its depth involves the interactions between computers and humans.