Data Science Dojo

AUGUST 19, 2024

Bureau of Labor Statistics predicting a 35% increase in job openings from 2022 to 2032. This is used for tasks like clustering, dimensionality reduction, and anomaly detection. For example, clustering customers based on their purchase history to identify different customer segments.

AI 278

AI AI Machine Learning Machine Learning 278

IBM Journey to AI blog

DECEMBER 20, 2023

Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms. Random forest algorithms —predict a value or category by combining the results from a number of decision trees.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

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? The information from previous decisions is analyzed via the decision tree.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

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? The information from previous decisions is analyzed via the decision tree.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Pickl AI

DECEMBER 14, 2023

In 2022, around 97% of the companies invested in Big Data and 91% of them invested in AI, clearly stamping that data is becoming the linchpin for successful business. Decision Trees Decision trees are a versatile statistical modelling technique used for decision-making in various industries.

Data Analysis 52

Data Analysis Data Analysis Decision Trees Analytics 52

Pickl AI

NOVEMBER 18, 2024

billion in 2022 and is expected to grow significantly, reaching USD 505.42 Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. billion by 2031 at a CAGR of 34.20%.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

SEPTEMBER 3, 2023

CAGR during 2022-2030. Density-Based Spatial Clustering of Applications with Noise (DBSCAN): DBSCAN is a density-based clustering algorithm. It identifies regions of high data point density as clusters and flags points with low densities as anomalies. Billion which is supposed to increase by 35.6%

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52