Data Science Dojo

FEBRUARY 14, 2024

In the world of data science and machine learning, feature transformation plays a crucial role in achieving accurate and reliable results. 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.

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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 (..)

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Towards AI

MARCH 21, 2023

By New Africa In this article, I will show how to implement a K-Nearest Neighbor classification with Tensorflow.js. KNN KNN (K-Nearest Neighbors) classification is a supervised machine learning algorithm used for classification tasks. TensorFlow.js TensorFlow.js

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Towards AI

MAY 1, 2024

We shall look at various types of machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can call their libraries in R studios, including executing the code. In-depth Documentation- R facilitates repeatability by analyzing data using a script-based methodology.

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Machine Learning Machine Learning K-nearest Neighbors Decision Trees 98

Dataconomy

MARCH 4, 2025

Data mining refers to the systematic process of analyzing large datasets to uncover hidden patterns and relationships that inform and address business challenges. It’s an integral part of data analytics and plays a crucial role in data science.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

Data Science Dojo

JANUARY 30, 2024

Nearest neighbor search algorithms : Efficiently retrieving the closest patient vec t o r s to a given query. Techniques like k-Nearest Neighbors ( kNN ) and Annoy trees excel in this area, enabling rapid identification of sim i l a r patients.

Database 361

Database K-nearest Neighbors Natural Language Processing Algorithm 361

Data Science Dojo

JULY 15, 2024

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. Decision Trees: These work by asking a series of yes/no questions based on data features to classify data points.

Machine Learning 307

Machine Learning Machine Learning Algorithm Clustering 307

ODSC - Open Data Science

DECEMBER 14, 2023

Photo Mosaics with Nearest Neighbors: Machine Learning for Digital Art In this post, we focus on a color-matching strategy that is of particular interest to a data science or machine learning audience because it utilizes a K-nearest neighbors (KNN) modeling approach. Learn more here!

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Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI.

Machine Learning 105

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 105

Pickl AI

MARCH 26, 2024

Nevertheless, its applications across classification, regression, and anomaly detection tasks highlight its importance in modern data analytics methodologies. The K Nearest Neighbors (KNN) algorithm of machine learning stands out for its simplicity and effectiveness. What are K Nearest Neighbors in Machine Learning?

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ODSC - Open Data Science

FEBRUARY 10, 2025

Convolutional neural networks offer high accuracy in video analysis but require considerable amounts of data. K-nearest neighbors are sufficient for detecting specific medialike in copyright protectionbut less reliable when analyzing a broad range of factors.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Natural Language Processing 52

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. In-depth Documentation- R facilitates repeatability by analyzing data using a script-based methodology.

Machine Learning 119

Machine Learning Machine Learning Algorithm K-nearest Neighbors 119

Dataversity

JUNE 4, 2021

The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. Click to learn more about author Kartik Patel. In this article, we will discuss the KNN Classification method of analysis.

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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.

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Data Analyst Data Science Machine Learning Machine Learning 52

ODSC - Open Data Science

FEBRUARY 3, 2023

Whether you are just cleaning your data for analysis, monitoring the health of your computer systems, looking out for cybersecurity threats, or sifting through claims and transactions looking for fraud, anomalies drastically impact any analysis to be done. LaBarr holds a B.S. in economics, as well as a B.S.,

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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?

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Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. Distance preserving embeddings: The name of this method is straightforward. The embedding space is generated by preserving the distances between the labels.

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K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

AWS Machine Learning Blog

APRIL 3, 2024

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality. Open the titan_mm_embed_search_blog.ipynb notebook.

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IBM Journey to AI blog

DECEMBER 20, 2023

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. 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

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 52

Python K-nearest Neighbors Data Science Data Visualization 52

Towards AI

FEBRUARY 19, 2025

Word2Vec, BERT, ResNet) and capture the semantic meaning or features of the data. But heres the catch scanning millions of vectors one by one (a brute-force k-Nearest Neighbors or KNN search) is painfully slow. These vectors are typically generated by machine learning models (e.g.,

Database 77

Database K-nearest Neighbors Machine Learning Machine Learning 77

PyImageSearch

JUNE 19, 2023

Figure 7: TF-IDF calculation (source: Towards Data Science ). K-Nearest Neighbor K-nearest neighbor (KNN) ( Figure 8 ) is an algorithm that can be used to find the closest points for a data point based on a distance measure (e.g.,

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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. To make this work, we need to transform the textual interactions into a format that allows algebraic operations.

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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 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

AWS Machine Learning Blog

APRIL 19, 2024

We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. He is also an adjunct lecturer in the MS data science and analytics program at Georgetown University in Washington D.C. An OpenSearch Service vector search is performed using these embeddings.

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AWS Machine Learning Blog

JUNE 9, 2023

“GraphStorm enables our team to train GNN embedding in a self-supervised manner on a graph with 288 million nodes and 2 billion edges,” Says Haining Yu, Principal Applied Scientist at Amazon Measurement, Ad Tech, and Data Science. python3 -m graphstorm.run.gs_link_prediction --inference --num_trainers 8 --part-config /data/oagv2.1/mag_bert_constructed/mag.json

ML 104

ML ML K-nearest Neighbors Machine Learning 104

AWS Machine Learning Blog

JANUARY 30, 2024

We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query. Setting k=1 retrieves the most relevant slide to the user question. He is also an adjunct lecturer in the MS data science and analytics program at Georgetown University in Washington D.C.

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Mlearning.ai

APRIL 6, 2023

In contrast, for datasets with low dimensionality, simpler algorithms such as Naive Bayes or K-Nearest Neighbors may be sufficient. ▶ Distribution of the Data : The distribution of the data can also affect the choice of algorithm.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

AWS Machine Learning Blog

JANUARY 6, 2023

In this post, we present a solution to handle OOC situations through knowledge graph-based embedding search using the k-nearest neighbor (kNN) search capabilities of OpenSearch Service. Solution overview. The key AWS services used to implement this solution are OpenSearch Service, SageMaker, Lambda, and Amazon S3.

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AWS Machine Learning Blog

APRIL 5, 2023

This includes preparing data, creating a SageMaker model, and performing batch transform using the model. Data overview and preparation You can use a SageMaker Studio notebook with a Python 3 (Data Science) kernel to run the sample code. For this post, we use the Amazon Berkeley Objects Dataset.

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Heartbeat

SEPTEMBER 22, 2023

K-Nearest Neighbors (KNN) Classifier: The KNN algorithm relies on selecting the right number of neighbors and a power parameter p. The n_neighbors parameter determines how many data points are considered for making predictions. random_state=0) 3.3.

Machine Learning 64

Machine Learning Machine Learning ML ML 64

Mlearning.ai

JULY 6, 2023

Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. Evaluation Matrix In SSL, the performance evaluation is usually conducted while doing transfer learning in the downstream task. Besides that, there is also qualitative evaluation.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

Open the notebook synthetic-data-generation.ipynb. Choose the default Python 3 kernel and Data Science 3.0 find_similar_items performs semantic search using the k-nearest neighbors (kNN) algorithm on the input image prompt. image, then follow the instructions in the notebook.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

We design a K-Nearest Neighbors (KNN) classifier to automatically identify these plays and send them for expert review. Thompson Bliss is a Manager, Football Operations, Data Scientist at the National Football League. Some plays are mixed into other coverage types, as shown in the following figure (right).

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ML ML Machine Learning Machine Learning 88

PyImageSearch

OCTOBER 30, 2023

Figure 6: Recurrent neural networks (source: Venkatachalam, 2019, Towards Data Science ). text mining, K-nearest neighbor, clustering, matrix factorization, and neural networks). Similarly, at each time step, the RNN produces an output as a non-linear function of input and previous hidden state. Gosthipaty, S.

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The MLOps Blog

DECEMBER 19, 2022

index.add(xb) # xq are query vectors, for which we need to search in xb to find the k nearest neighbors. # The search returns D, the pairwise distances, and I, the indices of the nearest neighbors. D, I = index.search(xq, k) #Source: [link] Check this out to learn more. . # Creating the index. While neptune.ai

ML 52

ML ML Algorithm Deep Learning 52

PyImageSearch

SEPTEMBER 16, 2024

Similarly, autoencoders can be trained to reconstruct input data, and data points with high reconstruction errors can be flagged as anomalies. Proximity-Based Methods Proximity-based methods can detect anomalies based on the distance between data points. This technique is widely used in various fields (e.g.,

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57