AWS Machine Learning Blog

SEPTEMBER 29, 2023

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region.

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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. Matthew Rhodes is a Data Scientist I working in the Amazon ML Solutions Lab. Solution overview.

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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. Regression modeling is a statistical tool used to find the relationship between labeled data and variable data.

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Snorkel AI

APRIL 11, 2023

So in these two plots, we actually calculated the largest connected component based on the K-nearest neighbor graph for different values of k and we plotted the CDF. And what this means is that we actually don’t need to look at all of the unlabeled data. AB : Got it. Thank you. CC : Oh, yes.

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Snorkel AI

APRIL 11, 2023

So in these two plots, we actually calculated the largest connected component based on the K-nearest neighbor graph for different values of k and we plotted the CDF. And what this means is that we actually don’t need to look at all of the unlabeled data. AB : Got it. Thank you. CC : Oh, yes.

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K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

So in these two plots, we actually calculated the largest connected component based on the K-nearest neighbor graph for different values of k and we plotted the CDF. And what this means is that we actually don’t need to look at all of the unlabeled data. AB : Got it. Thank you. CC : Oh, yes.

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K-nearest Neighbors Clustering Deep Learning Deep Learning 52

AWS Machine Learning Blog

AUGUST 26, 2024

This hands-on experience allowed our developers and data scientists to gain practical knowledge and understanding of the capabilities and limitations of generative AI. Foster continuous learning – In the early stages of our generative AI journey, we encouraged our teams to experiment and build prototypes across various domains.

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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. NumPy also provides a number of mathematical functions that can be used to operate on arrays, such as addition, subtraction, multiplication, and division.

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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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AWS Machine Learning Blog

APRIL 5, 2023

If you want to ingest data from Amazon S3 into OpenSearch Service at scale, you can launch an Amazon SageMaker Processing job with the appropriate instance type and instance count. She has been working with diverse range of customers to provide architectural guidance and help them to deliver effective AI/ML solution via data lab engagement.

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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. We pay our contributors, and we don’t sell ads.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

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Pickl AI

DECEMBER 9, 2024

Summary: Inductive bias in Machine Learning refers to the assumptions guiding models in generalising from limited data. By managing inductive bias effectively, data scientists can improve predictions, ensuring models are robust and well-suited for real-world applications.

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Snorkel AI

APRIL 28, 2023

You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. That is something that, with this k-nearest neighbor proxy thing, to a certain extent we are able to achieve. You’ll have different shapes of these pipelines.

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Snorkel AI

APRIL 28, 2023

You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. That is something that, with this k-nearest neighbor proxy thing, to a certain extent we are able to achieve. You’ll have different shapes of these pipelines.

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Pickl AI

SEPTEMBER 12, 2024

Data Science is the art and science of extracting valuable information from data. It encompasses data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights that can drive decision-making and innovation.

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Pickl AI

NOVEMBER 18, 2024

K-Nearest Neighbors), while others can handle large datasets efficiently (e.g., It offers extensive support for Machine Learning, data analysis, and visualisation. It’s also important to consider the algorithm’s complexity, the model’s interpretability, and its scalability. Random Forests).

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Pickl AI

JULY 26, 2023

Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models. Such models may perform exceedingly well on the training data but poorly on unseen data, indicating a lack of generalization.

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Machine Learning Machine Learning Cross Validation Decision Trees 52

The MLOps Blog

DECEMBER 19, 2022

As Data Scientists, we all have worked on an ML classification model. 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. Creating the index.

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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. If the dataset is very large, then it becomes cumbersome to run data on it.

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Data Science Decision Trees Machine Learning Machine Learning 52

Dataconomy

SEPTEMBER 1, 2023

Researchers often experiment with various algorithms like random forest, K-nearest neighbor, and logistic regression to find the best combination. Effective collaboration between medical experts, data scientists, and machine learning researchers is instrumental in driving innovation.

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Dataconomy

APRIL 18, 2025

With time series data often being used in areas like economics, finance, and environmental science, understanding and addressing these gaps is crucial for informed decision-making. Missing data can lead to biased results and misinterpretations, making it vital for data scientists to develop strategies for handling them.

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