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Python for machine learning projects

Track :

Computer Science

Course Presenter :

Great Learning

Lessons no : 3

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What will you learn in this course?
  • Develop machine learning models using Python libraries like Scikit-Learn, TensorFlow, and Pandas for classification, regression, and clustering tasks
  • Apply data preprocessing, feature engineering, and data visualization techniques to improve model accuracy and insights
  • Implement model evaluation, hyperparameter tuning, and cross-validation to optimize machine learning performance
  • Deploy machine learning models using Flask or Streamlit for real-world applications and automation

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Lessons | 3


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Python for machine learning projects, in this course you'll learn how to build real-world machine learning models using Scikit-Learn, TensorFlow, and Pandas. Master essential concepts like data preprocessing, feature engineering, model training, and evaluation. Work on hands-on projects covering classification, regression, clustering, and deep learning. Understand how to optimize models using hyperparameter tuning, cross-validation, and performance metrics. Learn to handle large datasets, automate workflows, and deploy ML models using Flask or Streamlit. By the end of the course, you'll be able to develop and deploy end-to-end machine learning projects using Python. Great Learning