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

Mlearning.ai

MAY 23, 2023

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.

Data Science 52

Data Science Decision Trees Deep Learning Deep Learning 52

DagsHub

APRIL 21, 2024

Scikit-learn Scikit-learn is a machine learning library in Python that is majorly used for data mining and data analysis. It also provides tools for model evaluation , including cross-validation, hyperparameter tuning, and metrics such as accuracy, precision, recall, and F1-score.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Mlearning.ai

JUNE 27, 2023

Originally used in Data Mining, clustering can also serve as a crucial preprocessing step in various Machine Learning algorithms. The optimal value for K can be found using ideas like Cross Validation (CV). How would we tackle this challenge? K = 3 ; 3 Clusters. K = No of clusters.

Clustering 52

Clustering Algorithm Data Analyst Machine Learning 52

Dataconomy

SEPTEMBER 1, 2023

Heart disease stands as one of the foremost global causes of mortality today, presenting a critical challenge in clinical data analysis. Leveraging hybrid machine learning techniques, a field highly effective at processing vast healthcare data volumes is increasingly promising in effective heart disease prediction.

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195