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Unlocking data science 101: The essential elements of statistics, Python, models, and more

Data Science Dojo

The flexibility of Python extends to its ability to integrate with other technologies, enabling data scientists to create end-to-end data pipelines that encompass data ingestion, preprocessing, modeling, and deployment. Decision trees are used to classify data into different categories.

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Comprehensive Guide to Data Anomalies

Pickl AI

Autoencoders These neural network architectures are used to learn efficient representations of data. By training an autoencoder on normal data, it can reconstruct input data. Support Vector Machines (SVM) SVM can be employed for anomaly detection by finding the hyperplane that best separates normal data from anomalies.

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What Does the Modern Data Scientist Look Like? Insights from 30,000 Job Descriptions

ODSC - Open Data Science

Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus. Tools like Apache Airflow are widely used for scheduling and monitoring workflows, while Apache Spark dominates big data pipelines due to its speed and scalability.

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques. Pipeline Orchestration Tools To handle the end-to-end workflow orchestration, you can use famous tools like Apache Airflow and Kubeflow Pipelines.

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How Active Learning Can Improve Your Computer Vision Pipeline

DagsHub

They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN). Both of these approaches use different methods to label the dataset for various computer vision tasks.  Libact : It is a Python package for active learning.