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What a data scientist should know about machine learning kernels?

Mlearning.ai

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. Supervised learning algorithms learn from labelled data, where each input is associated with a corresponding output label.

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Feature Selection Techniques in Machine Learning

Pickl AI

RFE works effectively with algorithms like Support Vector Machines (SVMs) and linear regression. Understanding these mathematical foundations allows data scientists to make informed decisions, improving model accuracy and interpretability. The process continues until the desired number of features is selected.

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Bias and Variance in Machine Learning

Pickl AI

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

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The Age of Health Informatics: Part 1

Heartbeat

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. Python supports diverse model validation and evaluation techniques, which are crucial for optimising model accuracy and generalisation.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. They are handy for high-dimensional data.