Machine Learning: Building a Linear Regression Model
Topics for this course
Machine Learning: Building a Linear Regression Model
-
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
Description
This Python and Machine Learning course provides a strong foundation in data analysis, statistics, and linear regression modeling. It begins with Python basics using Google Colab, covering variables, NumPy, Pandas, and data visualization with Seaborn. Learners explore statistical concepts, data exploration, handling missing values and outliers, and feature engineering. The course introduces machine learning concepts with a deep focus on linear regression, including simple, multiple, ridge, and lasso regression. Practical implementation in Python, model evaluation, bias-variance tradeoff, and business problem understanding ensure learners gain real-world, job-ready analytical and predictive modeling skills.
