Pickl AI

FEBRUARY 2, 2025

Summary: Associative classification in data mining combines association rule mining with classification for improved predictive accuracy. Despite computational challenges, its interpretability and efficiency make it a valuable technique in data-driven industries. Lets explore each in detail.

Data Mining 52

Data Mining Data Mining Data Mining Decision Trees 52

Dataconomy

JUNE 13, 2023

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.

Power BI 103

Power BI Data Analysis Data Analysis Artificial Intelligence 103

IBM Journey to AI blog

JULY 6, 2023

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 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

SEPTEMBER 23, 2024

Applications of Data Science Data Science is not confined to one sector; its applications span multiple industries, transforming organisations’ operations. From healthcare to marketing, Data Science drives innovation by providing critical insights.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Pickl AI

JULY 12, 2024

Pandas: A powerful library for data manipulation and analysis, offering data structures and operations for manipulating numerical tables and time series data. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Heartbeat

OCTOBER 23, 2023

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.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52