Description:
By the end of this course you will be able to:
Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU.
Start Today with a 7-Day Free Trial
- Taught by top companies and universities
- Affordable programs and 7 day free trial
- Apply your skills with hands-on projects
- Learn on your own schedule
- Course videos and readings
- Graded quizzes and assignments
- No degree or experience required for many programs
- Shareable Certificate upon completion
Curriculum is empty
Free