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)
1.0

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
  • Course Number:
    it_sdpyai_03_enus

    Expertise Level
    Intermediate