AI and ML Solutions with Python: Deep Learning and Neural Network Implementation Overview/Description Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description Discover how to implement neural network with data sampling and workflow models using scikit-learn, and explore the pre and post model approaches of implementing machine learning workflows.
Expected Duration (hours)
1.1
Lesson Objectives AI and ML Solutions with Python: Deep Learning and Neural Network Implementation
implement recurrent neural network
work with data sampling
implement dimensionality reduction with PCA
demonstrate how to use the Gaussian processes for regression
describe the core concepts and features of Linear model
identify the pre-model and post-model workflow in analytics
work with Classification and Bayesian Ridge regression using scikit-learn
describe the core concept of Linear Regression model
demonstrate how to implement Logistic regression using linear methods
create and fit linear regression on a dataset and get the feature coefficient
Course Number: it_sdpyai_03_enus
Expertise Level
Intermediate