Data Science Dojo

FEBRUARY 14, 2024

Feature Engineering is a process of using domain knowledge to extract and transform features from raw data. These features can be used to improve the performance of Machine Learning Algorithms. In the world of data science and machine learning, feature transformation plays a crucial role in achieving accurate and reliable results.

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Data Science Dojo

MAY 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

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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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Data Science Dojo

JANUARY 30, 2024

The search involves a combination of various algorithms, like approximate nearest neighbor optimization, which uses hashing, quantization, and graph-based detection. Nearest neighbor search algorithms : Efficiently retrieving the closest patient vec t o r s to a given query.

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Database K-nearest Neighbors Natural Language Processing Algorithm 361

Towards AI

MAY 1, 2024

In this piece, we shall look at tips and tricks on how to perform particular GIS machine learning algorithms regardless of your expertise in GIS, if you are a fresh beginner with no experience or a seasoned expert in geospatial machine learning. Load required librarieslibrary(sf) # spatial datalibrary(raster) # for raster manipulation 1.

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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.

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Data Mining Data Mining Data Mining Decision Trees 91

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.

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Machine Learning Machine Learning Algorithm K-nearest Neighbors 119

Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Nevertheless, its applications across classification, regression, and anomaly detection tasks highlight its importance in modern data analytics methodologies.

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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? For example, it takes millions of images and runs them through a training algorithm.

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Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 105

ODSC - Open Data Science

FEBRUARY 10, 2025

Choose an Appropriate Algorithm As with all machine learning processes, algorithm selection is also crucial. Convolutional neural networks offer high accuracy in video analysis but require considerable amounts of data. Consider the unique advantages and disadvantages of each input type to find whats right for yourgoals.

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

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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Dataversity

JUNE 4, 2021

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. Click to learn more about author Kartik Patel.

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Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

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K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

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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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. 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.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Mlearning.ai

APRIL 6, 2023

However, with a wide range of algorithms available, it can be challenging to decide which one to use for a particular dataset. will my data help in this ? If you can understand the data then you even do not need to use all algorithms. Let's understand this in detail.

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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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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. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.

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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.

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Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

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 analyzing how users have interacted with items in the past, we can use algorithms to approximate the utility function and make personalized recommendations that users will love.

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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. 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. 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., 💡 Why?

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Database K-nearest Neighbors Machine Learning Machine Learning 70

Heartbeat

SEPTEMBER 22, 2023

Introduction In the world of machine learning, where algorithms learn from data to make predictions, it’s important to get the best out of our models. Steps to Perform Hyperparameter Tuning Hyperparameter Tuning process (Image by author) Select Hyperparameters to Tune: Different algorithms have different hyperparameters.

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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

Introducing GraphStorm Graph algorithms and graph ML are emerging as state-of-the-art solutions for many important business problems like predicting transaction risks, anticipating customer preferences, detecting intrusions, optimizing supply chains, social network analysis, and traffic prediction. on the test set of the constructed graph.

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ML ML K-nearest Neighbors Machine Learning 95

PyImageSearch

OCTOBER 30, 2023

We will now examine how Spotify uses these data sources and advance machine learning techniques to address the music recommendation problem. Spotify’s Discover Weekly ( Figure 3 ) is an algorithm-generated playlist released every Monday to offer its listeners custom, curated music recommendations. to train their algorithm.

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K-nearest Neighbors Algorithm Clustering Machine Learning 67

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 56

Clustering Algorithm Machine Learning Machine Learning 56

The MLOps Blog

DECEMBER 19, 2022

Lesson 1: Mitigating data sparsity problems within ML classification algorithms What are the most popular algorithms used to solve a multi-class classification problem? So, the model might not have a sufficient number of data samples to learn the pattern for each class. Let’s take a look at some of them.

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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. We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. The results show that most of them were indeed labeled incorrectly.

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Mlearning.ai

JUNE 6, 2023

HOGs are great feature detectors and can also be used for object detection with SVM but due to many other State of the Art object detection algorithms like YOLO, SSD, present out there, we don’t use HOGs much for object detection. I have used Boston Housing Data for this use case. This is a simple project.

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Towards AI

JULY 19, 2023

A technical overview of solving this problem goes like this — You can assign a penalty to the misclassification of the minority class (The one with the lesser proportion) and by doing so, allow the algorithm to learn this penalization. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc.,

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