ML: Concepts & Applications
Learn to use Python and libraries for data tasks, understand key machine learning techniques, and apply them to real-world datasets for a strong research foundation.
Unsupervised Learning,Artificial Neural Network,Machine Learning,regression,Statistical Classification
Description for ML: Concepts & Applications
- Acquire the skills necessary to utilize Python and libraries such as Scikit-learn, TensorFlow, and Pandas for data ingestion, investigation, preparation, and modeling.
- Support vector machines, decision trees and ensembles, clustering, PCA, hidden Markov models, deep learning, and linear regression are among the techniques that are employed to train and evaluate models.
- Acquire a conceptual understanding of these techniques in order to understand the significance and reasoning behind the results.
- Based on an introductory machine learning course from the University of Chicago, work with real-world datasets, primarily from public policy, to establish a foundation for advanced research.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by The University of Chicago
Duration: 3 weeks at 12 hours a week
Schedule: Flexible
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