# Deep Learning & Neural Network Implementation

Python 3.6.5    |    Intermediate
• 10 videos | 1h 2m 11s
• Includes Assessment
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

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

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