Course Content
Machine Learning: Building a Linear Regression Model
0/50
Course Contents
07:28
Colab Introduction
04:36
Arthmetic Operation
04:58
Variables and Strings
13:35
Indexing
05:30
Numpy
11:51
Pandas
06:29
Seaborn
06:43
Types of data
04:03
Types of Statistics
01:55
Frequency Distribution
09:50
Measures of centers
05:16
Measures of dispersion
03:17
Intro to machine learning
16:33
Gathering business knowledge
02:13
Data exploration
02:31
Importing data in python
06:04
The dataset and the data dictionary
06:40
Univariate analysis
01:03
Edd in python
11:41
Outlier Treatment
06:05
Outlier Treatment Part-2
12:49
Missing values
04:37
Missing values imputation in python
02:14
Seasonality
04:18
Bi variate analysis and variable transformation
10:09
Variable transformation and deletion in python
10:54
Non usable variables
03:35
Dummy variable creation handling qualitative data
05:51
Correlation Analysis
07:53
Correlation analysis in python
06:08
Dummy variable creation in python
05:03
The problem statement
01:31
Basic equations and ordinary least squares method
09:51
Assessing accuracy of predicted coefficients
13:02
Assessing model accuracy RSE and R squared original
07:40
Simple linear regression in python
11:07
Multiple linear regression
05:03
The F-statistic
07:21
Interpreting results of categorical variables
04:48
Multiple linear regression in python
05:29
Test train split
08:43
Bias variance trade off
06:20
Test train split in python
07:32
Linear models other than ols
03:37
Subset selection techniques
09:53
Shrinkage methods ridge and lasso
05:41
Heteroscedasticity
02:47
Ridge regression and lasso in python
12:43
Machine Learning: Building a Linear Regression Model Quiz
Machine Learning: Building a Linear Regression Model
0%
Complete
Mark as Complete