Deep Learning & Neural Network Implementation
Python 3.6.5
| Intermediate
- 10 Videos | 1h 6m 41s
- Includes Assessment
- Earns a Badge
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
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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
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1.Recurrent Neural Network7m 28sUP NEXT
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2.Data Sampling6m 42s
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3.Applying PCA10m 42s
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4.Gaussian Regression Process6m 38s
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5.Linear Model3m 14s
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6.Pre-Model and Workflow5m 21s
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7.Classification and Bayesian Ridge5m 47s
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8.Linear Regression Modelling5m 20s
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9.Logistic Regression Using Linear Method6m 46s
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10.Exercise: Working with Linear Regression4m 15s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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