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Machine Learning Simplilearn

Track :

Programming

Lessons no : 82

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What will you learn in this course?
  • Develop practical machine learning models using Python and popular libraries like scikit-learn and TensorFlow
  • Apply supervised and unsupervised learning techniques to real-world datasets for predictive analytics
  • Implement data preprocessing, feature engineering, and model evaluation to improve machine learning accuracy
  • Identify appropriate algorithms for classification, regression, clustering, and dimensionality reduction tasks
  • Optimize machine learning models through hyperparameter tuning and cross-validation methods
  • Interpret machine learning results to make data-driven decisions in business and technology contexts
  • Utilize machine learning workflows for automation and scalable data analysis solutions
  • Understand ethical considerations and bias mitigation in machine learning applications
  • Deploy machine learning models into production environments using cloud platforms and APIs
  • Analyze large datasets efficiently with data cleaning, normalization, and transformation techniques
  • Compare different machine learning algorithms to select the best model for specific problems
  • Implement ensemble methods like bagging and boosting to enhance model performance
  • Troubleshoot common issues in machine learning projects, including overfitting and underfitting
  • Stay updated with emerging trends and tools in machine learning for continuous professional growth

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Lessons | 82
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TAKIRE MADHUSUDHAN

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2025-11-20

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Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting .