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The Age of Health Informatics: Part 1

Heartbeat

We will examine real-life applications where health informatics has outperformed traditional methods, discuss recent advances in the field, and highlight machine learning tools such as time series analysis with ARIMA and ARTXP that are transforming health informatics.

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

IBM Journey to AI blog

Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem. What is machine learning? It requires data science tools to first clean, prepare and analyze unstructured big data.

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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? Popular algorithms for topic modeling include Latent Dirichlet Allocation (LDA) and non-negative matrix factorization (NMF).

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Text Classification Using Machine Learning Algorithm in R

Heartbeat

Data mining, text classification, and information retrieval are just a few applications. To extract themes from a corpus of text data and then use these themes as features in text classification algorithms, topic modeling can be used in text classification. Naive Bayes is commonly used for spam classification.

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

DagsHub

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. What is MLOps?

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Classification vs. Clustering

Pickl AI

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification.

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Some algorithms that have low bias are Decision Trees, SVM, etc.