Decision Trees Predictive Analytics Support Vector Machines 
Pickl AI
JANUARY 21, 2025
Summary: Machine Learning algorithms enable systems to learn from data and improve over time. Key examples include Linear Regression for predicting prices, Logistic Regression for classification tasks, and Decision Trees for decision-making. Linear Regression predicts continuous outcomes, like housing prices.
Pickl AI
FEBRUARY 2, 2025
It identifies hidden patterns in data, making it useful for decision-making across industries. Compared to decision trees and SVM, it provides interpretable rules but can be computationally intensive. Key applications include fraud detection, customer segmentation, and medical diagnosis.
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Data Science Dojo
AUGUST 11, 2023
They play a pivotal role in predictive analytics and machine learning, enabling data scientists to make informed forecasts and decisions based on historical data patterns. By leveraging models, data scientists can extrapolate trends and behaviors, facilitating proactive decision-making.
Dataconomy
APRIL 4, 2023
Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!
Dataconomy
APRIL 4, 2023
Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!
Pickl AI
SEPTEMBER 16, 2024
Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Introduction Machine Learning (ML) has rapidly evolved over the past few years, becoming an integral part of various industries, from healthcare to finance.
IBM Journey to AI blog
DECEMBER 20, 2023
Supervised learning is commonly used for risk assessment, image recognition, predictive analytics and fraud detection, and comprises several types of algorithms. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g., temperature, salary).
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