Machine Learning Basics with Python

Master the basics of Machine Learning in Python and use Regression Models to solve business problems
Introduction
Welcome to the course!
3 mins
Course contents
6 mins
Course Resourses
8.88 MB
Basics of Statistics
Types of Data
5 mins
Types of Statistics
3 mins
Describing data Graphically
12 mins
Measures of Centers
8 mins
Measures of Dispersion
5 mins
Setting up Python and Jupyter Notebook
Installing Python and Anaconda
4 mins
Opening Jupyter Notebook
10 mins
Introduction to Jupyter
14 mins
Arithmetic operators in Python: Python Basics
5 mins
Strings in Python: Python Basics
20 mins
Lists, Tuples and Directories: Python Basics
19 mins
Working with Numpy Library of Python
12 mins
Working with Pandas Library of Python
10 mins
Working with Seaborn Library of Python
9 mins
Introduction to Machine Learning
Introduction to Machine Learning
17 mins
Building a Machine Learning Model
9 mins
Data Preprocessing
Gathering Business Knowledge
4 mins
Data Exploration
4 mins
The Dataset and the Data Dictionary
8 mins
Study Material and datasets
8.85 MB
Importing Data in Python
7 mins
Univariate analysis and EDD
4 mins
EDD in Python
13 mins
Outlier Treatment
5 mins
Outlier Treatment in Python
15 mins
Missing Value Imputation
4 mins
Missing Value Imputation in Python
5 mins
Seasonality in Data
4 mins
Bi-variate analysis and Variable transformation
17 mins
Variable transformation and deletion in Python
10 mins
Non-usable variables
5 mins
Dummy variable creation: Handling qualitative data
5 mins
Dummy variable creation in Python
6 mins
Correlation Analysis
11 mins
Correlation Analysis in Python
8 mins
Linear Regression
The Problem Statement
2 mins
Basic Equations and Ordinary Least Squares (OLS) method
9 mins
Assessing accuracy of predicted coefficients
15 mins
Assessing Model Accuracy: RSE and R squared
8 mins
Simple Linear Regression in Python
15 mins
Multiple Linear Regression
5 mins
The F - statistic
9 mins
Interpreting results of Categorical variables
6 mins
Multiple Linear Regression in Python
15 mins
Test-train split
10 mins
Bias Variance trade-off
7 mins
Test train split in Python
11 mins
Other Linear Models
Linear models other than OLS
5 mins
Subset selection techniques
12 mins
Shrinkage methods: Ridge and Lasso
8 mins
Ridge regression and Lasso in Python
24 mins
Extras: Heteroscedasticity
3 mins