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Unleashing the Power of Applied Text Mining in Python: Revolutionize Your Data Analysis

Pickl AI

Introduction to Applied Text Mining in Python Before going ahead, it is important to understand, What is Text Mining in Python? Text mining is also known as text analytics or Natural Language Processing (NLP). Visualize the sentiment distribution and analyze trends and patterns in the data.

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Elevating business decisions from gut feelings to data-driven excellence

Dataconomy

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

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The Age of Health Informatics: Part 1

Heartbeat

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Leveraging user-generated social media content with text-mining examples

IBM Journey to AI blog

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Text analysis takes it a step farther by focusing on pattern identification across large datasets, producing more quantitative results.

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Text Classification Using Machine Learning Algorithm in R

Heartbeat

Source: Author Introduction Text classification, which involves categorizing text into specified groups based on its content, is an important natural language processing (NLP) task. Data mining, text classification, and information retrieval are just a few applications. References Nagesh, Singh Chauhan.

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Classification vs. Clustering

Pickl AI

Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification. Certainly, these predictions and classification help in uncovering valuable insights in data mining projects. Hyperplanes are useful in separating the data points into groups.