Deep Learning & Neural Network Implementation

Python    |    Intermediate
  • 10 videos | 1h 2m 11s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 233 users Rating 4.5 of 233 users (233)
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

  • 7m 28s
    In this video, find out how to implement a recurrent neural network. FREE ACCESS
  • 6m 42s
    In this video, you will learn how to work with data sampling. FREE ACCESS
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    3.  Applying PCA
    10m 42s
    In this video, you will learn how to implement dimensionality reduction with PCA. FREE ACCESS
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    4.  Gaussian Regression Process
    6m 38s
    In this video, you will learn how to use Gaussian processes for regression. FREE ACCESS
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    5.  Linear Model
    3m 14s
    After completing this video, you will be able to describe the core concepts and features of a Linear model. FREE ACCESS
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    6.  Pre-Model and Workflow
    5m 21s
    During this video, you will learn how to identify the pre-model and post-model workflow in analytics. FREE ACCESS
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    7.  Classification and Bayesian Ridge
    5m 47s
    Find out how to work with classification and Bayesian ridge regression using scikit-learn. FREE ACCESS
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    8.  Linear Regression Modelling
    5m 20s
    Upon completion of this video, you will be able to describe the core concept of the Linear Regression model. FREE ACCESS
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    9.  Logistic Regression Using Linear Method
    6m 46s
    In this video, you will learn how to implement Logistic regression using linear methods. FREE ACCESS
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    10.  Exercise: Working with Linear Regression
    4m 15s
    In this video, you will create a linear regression model and fit it to a dataset. You will also get the feature coefficient. FREE ACCESS

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