Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
Instructed by Ryan Ahmed 10.5 hours on-demand video, 3 articles & 1 downloadable resource
After completing the course, you will…
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Master Python programming and Scikit learn as applied to machine learning regression
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Understand the underlying theory behind simple and multiple linear regression techniques
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Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy
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Apply multiple linear regression to predict stock prices and Universities acceptance rate
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Cover the basics and underlying theory of polynomial regression
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Apply polynomial regression to predict employees’ salary and commodity prices
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Understand the theory behind logistic regression
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Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features
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Understand the underlying theory and mathematics behind Artificial Neural Networks
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Learn how to train network weights and biases and select the proper transfer functions
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Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
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Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
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Apply ANNs to predict house prices given parameters such as area, number of rooms..etc
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Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test
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Understand the underlying theory and intuition behind Lasso and Ridge regression techniques
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Sample real-world, practical projects
Who this course is for:
- Data Scientists who want to apply their knowledge on Real World Case Studies
- Machine Learning Enthusiasts who look to add more projects to their Portfolio
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