Remove Data Mining Remove Deep Learning Remove Support Vector Machines
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Understanding the Synergy Between Artificial Intelligence & Data Science

Pickl AI

Synergy Between Artificial Intelligence and Data Science AI and Data Science complement each other through their unique but interconnected roles in data processing and analysis. Data Science involves extracting insights from structured and unstructured data using statistical methods, data mining, and visualisation techniques.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

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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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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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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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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.