Machine Learning Regression Masterclass In Python

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras

Last updated 2022-01-10 | 4.7

- 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

What you'll 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

* Requirements

* Machine Learning basics
* PC with Internet connetion

Description

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