Towards AI

APRIL 4, 2024

Created by the author with DALL E-3 Machine learning algorithms are the “cool kids” of the tech industry; everyone is talking about them as if they were the newest, greatest meme. Shall we unravel the true meaning of machine learning algorithms and their practicability?

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K-nearest Neighbors Machine Learning Machine Learning Decision Trees 81

Dataconomy

FEBRUARY 26, 2025

Step-by-step process Collect observational data: Gather extensive datasets that track various events over time to inform causal relationships. Discover causal relationships: Algorithms sift through data, uncovering connections that signify causality rather than simply correlation.

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K-nearest Neighbors AI AI Artificial Intelligence 91

Dataversity

JUNE 4, 2021

In this article, we will discuss the KNN Classification method of analysis. What Is the KNN Classification Algorithm? The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories.

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IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

Machine Learning 92

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AWS Machine Learning Blog

FEBRUARY 25, 2025

Its internal deployment strengthens our leadership in developing data analysis, homologation, and vehicle engineering solutions. 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.

Algorithm 86

Algorithm Machine Learning Machine Learning K-nearest Neighbors 86

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. It entails developing computer programs that can improve themselves on their own based on expertise or data. What is Unsupervised Machine Learning?

Machine Learning 52

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IBM Journey to AI blog

DECEMBER 19, 2023

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. If a data point appears further away from a dense section of points, it is considered an anomaly.

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PyImageSearch

JUNE 19, 2023

Each service uses unique techniques and algorithms to analyze user data and provide recommendations that keep us returning for more. By the end of the lesson, readers will have a solid grasp of the underlying principles that enable these applications to make suggestions based on data analysis.

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ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Pickl AI

SEPTEMBER 3, 2023

Anomaly Detection in Machine Learning: An approach to data analysis and Machine Learning called “anomaly detection,” also referred to as “outlier detection,” focuses on finding data points or patterns that considerably differ from what is considered to be “normal” or anticipated behaviour.

Machine Learning 52

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. It involves algorithms that identify and use data patterns to make predictions or decisions based on new, unseen data.

Machine Learning 52

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Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

PyImageSearch

SEPTEMBER 16, 2024

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? Jump Right To The Downloads Section Understanding Anomaly Detection: Concepts, Types, and Algorithms What Is Anomaly Detection? Looking for the source code to this post?

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57

Mlearning.ai

FEBRUARY 15, 2023

This data shows promise for the binary classifier that will be built. Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 5 shows the methods used to perform these data preprocessing techniques.

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Cross Validation Decision Trees Algorithm Natural Language Processing 52

Mlearning.ai

MAY 23, 2023

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data. What is the main advantage of sampling?

Data Science 52

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Towards AI

JULY 19, 2023

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. In our exercise, we will try to deal with this imbalance by — Using a stratified k-fold cross-validation technique to make sure our model’s aggregate metrics are not too optimistic (meaning: too good to be true!)

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Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Dataconomy

SEPTEMBER 1, 2023

Heart disease stands as one of the foremost global causes of mortality today, presenting a critical challenge in clinical data analysis. Leveraging hybrid machine learning techniques, a field highly effective at processing vast healthcare data volumes is increasingly promising in effective heart disease prediction.

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