Data Science Dojo

MAY 27, 2024

A prominent example is Google’s Duplex , a technology that enables AI assistants to make phone calls on behalf of users for tasks like scheduling appointments and reservations.

K-nearest Neighbors 338

K-nearest Neighbors AI AI Supervised Learning 338

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? Often, these trees adhere to an elementary if/then structure.

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? Often, these trees adhere to an elementary if/then structure.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Pickl AI

DECEMBER 9, 2024

Inductive bias helps in this process by limiting the search space, making it computationally feasible to find a good solution. In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds. For problems with well-defined local structures, k-NN may be the best fit.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. Uysal and Gunal, 2014).

Cross Validation 52

Cross Validation Decision Trees Natural Language Processing Algorithm 52

Pickl AI

NOVEMBER 18, 2024

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52