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Top 8 Machine Learning Algorithms

Data Science Dojo

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. Text Analysis: Feature extraction might involve extracting keywords, sentiment scores, or topic information from text data for tasks like sentiment analysis or document classification.

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Text Classification in NLP using Cross Validation and BERT

Mlearning.ai

Figure 5 Feature Extraction and Evaluation Because most classifiers and learning algorithms require numerical feature vectors with a fixed size rather than raw text documents with variable length, they cannot analyse the text documents in their original form.

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How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

These included document translations, inquiries about IDIADAs internal services, file uploads, and other specialized requests. This approach allows for tailored responses and processes for different types of user needs, whether its a simple question, a document translation, or a complex inquiry about IDIADAs services.

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An Essential Introduction to SVM Algorithm in Machine Learning

Pickl AI

Summary: Support Vector Machine (SVM) is a supervised Machine Learning algorithm used for classification and regression tasks. Among the many algorithms, the SVM algorithm in Machine Learning stands out for its accuracy and effectiveness in classification tasks. What is the SVM Algorithm in Machine Learning?

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Hyperparameters in Machine Learning: Categories  & Methods

Pickl AI

They vary significantly between model types, such as neural networks , decision trees, and support vector machines. Combine with cross-validation to assess model performance reliably. They define the model’s capacity to learn and how it processes data.

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What a data scientist should know about machine learning kernels?

Mlearning.ai

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents. Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. classification, regression) and data characteristics.