Deep Learning & Neural Network Implementation
Python 3.6.5
| Intermediate
- 10 Videos | 1h 2m 11s
- 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 networkwork with data samplingimplement dimensionality reduction with PCAdemonstrate how to use the Gaussian processes for regressiondescribe the core concepts and features of Linear model
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identify the pre-model and post-model workflow in analyticswork with Classification and Bayesian Ridge regression using scikit-learndescribe the core concept of Linear Regression modeldemonstrate how to implement Logistic regression using linear methodscreate 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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