 # Course: Machine Learning Python: Regression Modeling

## Machine Learning Python: Regression Modeling

• Life Time Access
• Certificate on Completion
• Access on Android and iOS App
• Self-Paced

Use Linear Regression to solve business problems and master the basics of Machine Learning

The course "Machine Learning Basics: Building Regression Model in Python" teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

In this course students will learn the following:

• How to predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
• How to do preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
• Understand how to interpret the result of Linear Regression model and translate them into actionable insight
• Understanding of basics of statistics and concepts of Machine Learning
• Learn advanced variations of OLS method of Linear Regression
• Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python

This course is suitable for anyone curious about machine learning or professionals beginning their data journey.

Basic knowledge
• Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
What you will learn

In this course students will learn the following:

• How to predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
• How to do preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
• Understand how to interpret the result of Linear Regression model and translate them into actionable insight
• Understanding of basics of statistics and concepts of Machine Learning
• Learn advanced variations of OLS method of Linear Regression
• Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python
Curriculum
Number of Lectures: 51
Total Duration: 07:13:43
Introduction
• Welcome to the course!
• Course contents
Basics of Statistics
• Types of Data
• Types of Statistics
• Describing data Graphically
• Measures of Centers
• Measures of Dispersion
Setting up Python and Jupyter Notebook
• Installing Python and Anaconda
• Opening Jupyter Notebook
• Introduction to Jupyter
• Arithmetic operators in Python: Python Basics
• Strings in Python: Python Basics
• Lists, Tuples and Directories: Python Basics
• Working with Numpy Library of Python
• Working with Pandas Library of Python
• Working with Seaborn Library of Python
Introduction to Machine Learning
• Introduction to Machine Learning
• Building a Machine Learning Model
Data Preprocessing
• Data Exploration
• The Dataset and the Data Dictionary
• Importing Data in Python
• Univariate analysis and EDD
• EDD in Python
• Outlier Treatment
• Outlier Treatment in Python
• Missing Value Imputation
• Missing Value Imputation in Python
• Bi-variate analysis and Variable transformation
• Variable transformation and deletion in Python
• Non-usable variables
• Dummy variable creation: Handling qualitative data
• Dummy variable creation in Python
• Correlation Analysis
• Correlation Analysis in Python
Linear Regression
• The Problem Statement
• Basic Equations and Ordinary Least Squares (OLS) method
• Assessing accuracy of predicted coefficients
• Assessing Model Accuracy: RSE and R squared
• Simple Linear Regression in Python
• Multiple Linear Regression
• The F - statistic
• Interpreting results of Categorical variables
• Multiple Linear Regression in Python
• Test-train split
• Test train split in Python
• Linear models other than OLS
• Subset selection techniques
• Shrinkage methods: Ridge and Lasso
• Ridge regression and Lasso in Python
Rating
Enrolled Students
(23)
Level
All
Price
\$ 9.00
Course Language
English
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