Objectives
- Frame a business use case as a machine learning problem.
- Describe how to improve data quality.
- Perform exploratory data analysis.
- Build and train supervised learning models.
- Optimize and evaluate models using loss functions and performance metrics.
- Create repeatable and scalable training, evaluation, and test datasets.
- Implement machine learning models using Keras and TensorFlow 2.x.
- Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization.
- Represent and transform features.
- Train models at scale with AI Platform.