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Machine Learning Lectures

Track :

Programming

Lessons no : 27

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What will you learn in this course?
  • Develop practical skills in designing and implementing machine learning algorithms for real-world data analysis and prediction
  • Apply supervised and unsupervised learning techniques to solve complex problems across industries like healthcare, finance, and marketing
  • Utilize Python and popular machine learning libraries such as scikit-learn, TensorFlow, and Keras for model development and deployment
  • Analyze and preprocess data effectively to improve machine learning model accuracy and efficiency
  • Evaluate model performance using metrics like accuracy, precision, recall, and F1 score for informed decision-making
  • Implement feature engineering and selection methods to enhance model robustness and predictive power
  • Understand the ethical considerations and biases in machine learning applications for responsible AI deployment
  • Optimize machine learning models through hyperparameter tuning and cross-validation for better generalization
  • Interpret machine learning models to extract actionable insights and support strategic business decisions
  • Troubleshoot common issues in machine learning workflows to ensure reliable and scalable solutions

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Lessons | 27


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Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given .