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Scikit Python

Track :

Programming

Lessons no : 18

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What will you learn in this course?
  • Implement machine learning algorithms using scikit-learn for classification, regression, and clustering tasks in Python
  • Apply data preprocessing, feature engineering, and model evaluation techniques with scikit-learn tools
  • Develop predictive models using scikit-learn's classification and regression algorithms for real-world datasets
  • Perform dimensionality reduction and feature selection to improve machine learning model performance
  • Utilize scikit-learn's model selection and hyperparameter tuning methods for optimized results
  • Analyze and visualize machine learning model outcomes using scikit-learn and Python libraries
  • Integrate scikit-learn into Python workflows for end-to-end machine learning projects
  • Troubleshoot common issues and improve model accuracy with scikit-learn best practices

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Lessons | 18


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Ganesh Mamidi 

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2025-06-23

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2025-06-21

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2025-06-20

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2024-06-10

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What is scikit-learn or sklearn? Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction .