Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

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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.

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Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

JULY 31, 2023

It constructs a hyperplane to separate different classes during training and uses it to make predictions on new data. Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 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.

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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.

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Decision Trees Machine Learning Machine Learning Support Vector Machines 195