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SKLearn and Python

Track :

Programming

Lessons no : 28

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What will you learn in this course?
  • Master how to implement machine learning algorithms using Python and scikit-learn for data analysis and AI projects
  • Apply Python programming skills to preprocess data, select features, and optimize machine learning models effectively
  • Utilize scikit-learn to build, train, and evaluate classification, regression, and clustering models in real-world scenarios
  • Develop proficiency in handling datasets, performing data cleaning, and feature engineering with Python for machine learning tasks
  • Implement model validation techniques such as cross-validation and hyperparameter tuning using scikit-learn
  • Leverage Python libraries to visualize data insights and model performance for better decision-making in AI projects
  • Understand the fundamentals of supervised and unsupervised learning algorithms with practical Python examples
  • Troubleshoot common issues in machine learning workflows using Python and scikit-learn tools
  • Integrate Python-based machine learning models into larger data analysis pipelines and applications
  • Enhance problem-solving skills by applying Python and scikit-learn to diverse data science challenges

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Lessons | 28


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Arju Singh

Nice
2024-07-20

RAGHU N

This is a good cource is good and the explination was good
2024-06-28

SKABDUL HAMEED NOVMAN

Good explaining
2023-12-18

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Python is also a leading language for data analysis and machine learning. While it is possible to use C++ for machine learning purposes as well, it is not a good option. In terms of simplicity, Python is much easier to use and has a great support system when it comes to AI and ML frameworks .