Data Science

ML Classification

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Learn through case studies, techniques, challenges, and objectives to master classification tasks, techniques, and metrics in Python for effective machine learning on various datasets.

Key AI Functions:Logistic Regression,Statistical Classification,Classification Algorithms,Decision Tree

Description for ML Classification

  • Case Studies: Engage in sentiment analysis and loan default prediction, representing classification tasks with broad applications like image classification and spam detection.
  • Techniques: Learn cutting-edge classification techniques such as logistic regression, decision trees, and boosting, along with employing stochastic gradient ascent for large-scale learning.
  • Challenges: Tackle real-world ML challenges like evaluating classifiers, handling missing data, and understanding precision-recall metrics.
  • Learning Objectives: Master classification model input-output, logistic regression, decision trees, boosting, and precision-recall metrics in Python, enabling effective classification on diverse datasets.
  • Level: Beginner

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by University of Washington

    Duration: 21 hours (approximately)

    Schedule: Flexible

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