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

Pickl AI

Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification. Text Mining Tools and Libraries Various tools and libraries have been developed to facilitate text-mining tasks.

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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. Machine learning and deep learning are both subsets of AI.

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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? And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

Moving the machine learning models to production is tough, especially the larger deep learning models as it involves a lot of processes starting from data ingestion to deployment and monitoring. It provides different features for building as well as deploying various deep learning-based solutions.

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Text Classification Using Machine Learning Algorithm in R

Heartbeat

Because of the package’s emphasis on tidy data, it is both a user-friendly option for those new to text analysis, and a valuable tool for experienced practitioners. You can learn more about the usage of the package here install.packages("tidytext") Application areas for topic modeling are numerous.