IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

JULY 31, 2023

Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. It constructs a hyperplane to separate different classes during training and uses it to make predictions on new data. What Are The Examples of Eager Learning Algorithms?

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Heartbeat

MARCH 8, 2023

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.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Mining 52

DagsHub

APRIL 21, 2024

Similar to TensorFlow, PyTorch is also an open-source tool that allows you to develop deep learning models for free. Scikit-learn Scikit-learn is a machine learning library in Python that is majorly used for data mining and data analysis.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Dataconomy

SEPTEMBER 1, 2023

Several data mining and neural network techniques have been employed to gauge the severity of heart disease but the prediction of it is a different subject. Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models.

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195