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

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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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