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4 Students
54 Lessons
Beginner

#### What Will You Learn?

• Learn how to solve real life problem using the Linear Regression technique
• Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
• Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
• Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
• Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python

#### Curriculum

7h 21m
09:42
##### Section 2: Setting up Python and Jupyter Notebook
1:38:02
Arithmetic operators in Python Python Basics
04:28
Strings in Python Python Basics
19:07
Lists Tuples and Directories Python Basics
18:41
Working with Numpy Library of Python
11:55
Working with Pandas Library of Python
09:15
Working with Seaborn Library of Python
08:58
##### Section 3: Basics of Statistics
30:11
Types of Data
04:05
Types of Statistics
02:46
Describing data Graphically
11:37
Measures of Centers
07:05
Measures of Dispersion
04:38
##### Section 4: Introduction to Machine Learning
24:46
Introduction to Machine Learning
16:03
Building a Machine Learning Model
08:43
##### Section 5: Data Preprocessing
2:04:55
Gathering Business Knowledge
03:26
Data Exploration
03:19
The Dataset and the Data Dictionary
07:31
Importing Data in Python
06:04
Univariate analysis and EDD
03:34
EDD in Python
12:11
Outlier Treatment
04:16
Outlier Treatment in Python
14:18
Missing Value Imputation
03:37
Missing Value Imputation in Python
04:57
Seasonality in Data
03:35
Bi-variate analysis and Variable transformation
16:14
Variable transformation and deletion in Python
09:21
Non-usable variables
04:44
Dummy variable creation Handling qualitative data
04:50
Dummy variable creation in Python
05:46
Correlation Analysis
10:05
Correlation Analysis in Python
07:07
##### Section 6: Linear Regression
2:33:47
The Problem Statement
01:26
Basic Equations and Ordinary Least Squares (OLS) method
08:13
Assessing accuracy of predicted coefficients
14:40
Assessing Model Accuracy RSE and R squared
07:20
Simple Linear Regression in Python
14:07
Multiple Linear Regression
04:58
The F - statistic
08:23
Interpreting results of Categorical variables
05:04
Multiple Linear Regression in Python
14:13
Test-train split
09:33
Bias Variance trade-off
06:02
Test train split in Python
10:19
Linear models other than OLS
04:19
Subset selection techniques
11:34
Shrinkage methods Ridge and Lasso
07:14
Ridge regression and Lasso in Python
23:51
Heteroscedasticity
02:31

#### Requirements

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

#### Abhishek and Pukhraj

Reviews 3
Students 152
Courses 5

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics, and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

#### BitDegree platform reviews

Our students say Excellent
9.5 out of 10