Remove Deep Learning Remove Exploratory Data Analysis Remove Support Vector Machines
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Pima Indian Diabetes Prediction

Heartbeat

I will start by looking at the data distribution, followed by the relationship between the target variable and independent variables. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

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Five machine learning types to know

IBM Journey to AI blog

Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Here is a brief description of the same.

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Enhancing Customer Churn Prediction with Continuous Experiment Tracking

Heartbeat

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Support Vector Machine (svm): Versatile model for linear and non-linear data.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.