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MACHINE LEARNING CON PYTHON

Track :

Programming

Lessons no : 57

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What will you learn in this course?
  • Develop practical machine learning models using Python libraries like scikit-learn, TensorFlow, and Keras for real-world applications
  • Implement data preprocessing, feature engineering, and data visualization techniques to improve machine learning model performance
  • Apply supervised and unsupervised learning algorithms to solve classification, regression, clustering, and anomaly detection problems
  • Evaluate model accuracy, precision, recall, and F1-score to optimize machine learning models effectively
  • Utilize Python to handle large datasets, perform data cleaning, and manage data pipelines for scalable machine learning projects
  • Design and deploy machine learning workflows using Python for automation and efficiency in various industries
  • Understand the fundamentals of neural networks and deep learning frameworks with Python for advanced AI solutions
  • Troubleshoot common issues in machine learning models, including overfitting, underfitting, and bias-variance tradeoff
  • Integrate machine learning models into web applications and APIs using Python for real-time data analysis
  • Apply best practices in model validation, cross-validation, and hyperparameter tuning to enhance model robustness
  • Leverage Python tools for natural language processing (NLP) and image recognition tasks in machine learning projects
  • Develop a comprehensive understanding of ethical considerations and data privacy in machine learning with Python

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Lessons | 57
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