Machine Learning Regression Masterclass in Python

Machine Learning Regression Masterclass in Python, available at $89.99, has an average rating of 4.41, with 83 lectures, based on 756 reviews, and has 6859 subscribers.

You will learn about Master Python programming and Scikit learn as applied to machine learning regression Understand the underlying theory behind simple and multiple linear regression techniques Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy Apply multiple linear regression to predict stock prices and Universities acceptance rate Cover the basics and underlying theory of polynomial regression Apply polynomial regression to predict employees’ salary and commodity prices Understand the theory behind logistic regression Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features Understand the underlying theory and mathematics behind Artificial Neural Networks Learn how to train network weights and biases and select the proper transfer functions Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance Apply ANNs to predict house prices given parameters such as area, number of rooms..etc 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 Understand the underlying theory and intuition behind Lasso and Ridge regression techniques Sample real-world, practical projects This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies or Machine Learning Enthusiasts who look to add more projects to their Portfolio It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies or Machine Learning Enthusiasts who look to add more projects to their Portfolio.

Enroll now: Machine Learning Regression Masterclass in Python

Summary

Title: Machine Learning Regression Masterclass in Python

Price: $89.99

Average Rating: 4.41

Number of Lectures: 83

Number of Published Lectures: 77

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 77

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

• Master Python programming and Scikit learn as applied to machine learning regression
• Understand the underlying theory behind simple and multiple linear regression techniques
• Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy
• Apply multiple linear regression to predict stock prices and Universities acceptance rate
• Cover the basics and underlying theory of polynomial regression
• Apply polynomial regression to predict employees’ salary and commodity prices
• Understand the theory behind logistic regression
• Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features
• Understand the underlying theory and mathematics behind Artificial Neural Networks
• Learn how to train network weights and biases and select the proper transfer functions
• Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
• Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
• Apply ANNs to predict house prices given parameters such as area, number of rooms..etc
• 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
• Understand the underlying theory and intuition behind Lasso and Ridge regression techniques
• Sample real-world, practical projects

Who Should Attend

• 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

Target Audiences

• 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