Data in Machine Learning
In brief, this course instructs students on the effective management of data biases, the prevention of overfitting, and the enhancement of model accuracy through the implementation of appropriate testing methods and feature engineering.
computer programming,python programming,machine learning,statistical analysis,linear algebra,ai & machine learning
Description for Data in Machine Learning
Essential Components of Data in Model Stages: Recognize the significance of data throughout various phases of model building, encompassing learning, training, and operation.
Prejudices and Data Origins: Acquire the ability to recognize biases in data and the sources that could affect the model's precision and equity.
Enhancing Model Generalization: Apply techniques to improve the generalization of your model, hence enhancing its performance on unfamiliar data.
Overfitting, Mitigation Strategies, and Evaluation Metrics: Comprehend the ramifications of overfitting and implement suitable mitigation solutions, in conjunction with efficient testing and validation techniques.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Alberta Machine Intelligence Institute
Duration: 3 weeks at 3 hours a week
Schedule: Flexible
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