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Machine Learning Questions

Track :

Programming

Lessons no : 28

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What will you learn in this course?
  • Develop and implement machine learning algorithms for predictive analytics and data-driven decision making using Python and R
  • Apply supervised and unsupervised learning techniques to real-world datasets for classification, regression, and clustering tasks
  • Evaluate machine learning model performance using metrics like accuracy, precision, recall, and F1 score for optimal results
  • Utilize feature engineering and data preprocessing methods to improve machine learning model accuracy and robustness
  • Identify appropriate machine learning algorithms for specific problems such as fraud detection, customer segmentation, and recommendation systems
  • Interpret model outputs and visualize results to communicate insights effectively to stakeholders
  • Implement cross-validation and hyperparameter tuning to enhance model generalization and prevent overfitting
  • Understand ethical considerations and biases in machine learning applications for responsible AI deployment
  • Leverage popular machine learning libraries and tools like scikit-learn, TensorFlow, and Keras for model development
  • Troubleshoot common issues in machine learning workflows to optimize model training and deployment processes

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Lessons | 28


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Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.