Deep Learning & Neural Network Implementation

Python 3.6.5    |    Intermediate
  • 10 Videos | 1h 6m 41s
  • Includes Assessment
  • Earns a Badge
Likes 51 Likes 51
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Recurrent Neural Network
    7m 28s
    UP NEXT
  • Playable
    2. 
    Data Sampling
    6m 42s
  • Locked
    3. 
    Applying PCA
    10m 42s
  • Locked
    4. 
    Gaussian Regression Process
    6m 38s
  • Locked
    5. 
    Linear Model
    3m 14s
  • Locked
    6. 
    Pre-Model and Workflow
    5m 21s
  • Locked
    7. 
    Classification and Bayesian Ridge
    5m 47s
  • Locked
    8. 
    Linear Regression Modelling
    5m 20s
  • Locked
    9. 
    Logistic Regression Using Linear Method
    6m 46s
  • Locked
    10. 
    Exercise: Working with Linear Regression
    4m 15s

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