Data Science, K-nearest Neighbors and Support Vector Machines

Data Science

K-nearest Neighbors

Support Vector Machines

Problem-solving tools offered by digital technology

Data Science Dojo

FEBRUARY 15, 2023

Image Credit: Pinterest – Problem solving tools In last week’s post , DS-Dojo introduced our readers to this blog-series’ three focus areas, namely: 1) software development, 2) project-management, and 3) data science. This week, we continue that metaphorical (learning) journey with a fun fact. Better yet, a riddle. IoT, Web 3.0,

K-nearest Neighbors

K-nearest Neighbors Decision Trees Support Vector Machines Data Science

Feature scaling: A way to elevate data potential

Data Science Dojo

FEBRUARY 14, 2024

In this blog, we will discuss one of the feature transformation techniques called feature scaling with examples and see how it will be the game changer for our machine learning model accuracy. In the world of data science and machine learning, feature transformation plays a crucial role in achieving accurate and reliable results.

K-nearest Neighbors

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

MAY 27, 2024

Some common models used are as follows: Logistic Regression – it classifies by predicting the probability of a data point belonging to a class instead of a continuous value Decision Trees – uses a tree structure to make predictions by following a series of branching decisions Support Vector Machines (SVMs) – create a clear decision (..)

K-nearest Neighbors

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Top 8 Machine Learning Algorithms

Data Science Dojo

JULY 15, 2024

Common Classification Algorithms: Logistic Regression: A popular choice for binary classification, it uses a mathematical function to model the probability of a data point belonging to a particular class. Decision Trees: These work by asking a series of yes/no questions based on data features to classify data points.

Machine Learning

Machine Learning Machine Learning Algorithm Clustering

How to Call Machine Learning Algorithms on R for Spatial Analysis.

Towards AI

JULY 15, 2024

R has simplified the most complex task of geospatial machine learning and data science. As GIS is slowly embracing data science, mastery of programming is very necessary regardless of your perception of programming. data = trainData) 5.

Machine Learning

Machine Learning Machine Learning Algorithm K-nearest Neighbors

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

Data Analyst

Data Analyst Data Science Machine Learning Machine Learning

An Overview of Extreme Multilabel Classification (XML/XMLC)

Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels.

K-nearest Neighbors

K-nearest Neighbors Algorithm Clustering Support Vector Machines

Five machine learning types to know

IBM Journey to AI blog

DECEMBER 20, 2023

What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Here, we’ll discuss the five major types and their applications.

Machine Learning

Machine Learning Machine Learning Supervised Learning Clustering

[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Data Science?

Data Science

Data Science Decision Trees Machine Learning Machine Learning

8 of the Top Python Libraries You Should be Using in 2024

ODSC - Open Data Science

JANUARY 5, 2024

What makes it popular is that it is used in a wide variety of fields, including data science, machine learning, and computational physics. Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools. Not a bad list right?

Python

Python K-nearest Neighbors Data Science Data Visualization

Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Journey to AI blog

DECEMBER 19, 2023

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

Machine Learning

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of treating each input as entirely unique, we can use a distance-based approach like k-nearest neighbors (k-NN) to assign a class based on the most similar examples surrounding the input. Diego Martn Montoro is an AI Expert and Machine Learning Engineer at Applus+ Idiada Datalab.

Algorithm

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How to Choose the Best Algorithm for Your Machine Learning Project

Mlearning.ai

APRIL 6, 2023

For larger datasets, more complex algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks may be more suitable. ▶ Type of Data : The type of data you have can also affect the choice of the classification algorithm.

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

JULY 19, 2023

among supervised models and k-nearest neighbors, DBSCAN, etc., Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among unsupervised models.

Machine Learning

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines

Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

SEPTEMBER 16, 2024

Supervised Learning These methods require labeled data to train the model. The model learns to distinguish between normal and abnormal data points. For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data.

Clustering

Clustering Algorithm Machine Learning Machine Learning

Data Science Current

Problem-solving tools offered by digital technology

Feature scaling: A way to elevate data potential

Webinars

Trending Sources

Generative vs Discriminative AI: Understanding the 5 Key Differences

Webinars

Top 8 Machine Learning Algorithms

How to Call Machine Learning Algorithms on R for Spatial Analysis.

Basic Data Science Terms Every Data Analyst Should Know

An Overview of Extreme Multilabel Classification (XML/XMLC)

Five machine learning types to know

[Updated] 100+ Top Data Science Interview Questions

8 of the Top Python Libraries You Should be Using in 2024

Anomaly detection in machine learning: Finding outliers for optimization of business functions

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

How to Choose the Best Algorithm for Your Machine Learning Project

Predicting Heart Failure Survival with Machine Learning Models — Part II

Credit Card Fraud Detection Using Spectral Clustering

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