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Machine Learning in Python
Linear regression
Welcome to Advanced Statistics! (0:28)
Welcome to the Course
Introduction to Regression Analysis (1:27)
The Linear Regression Model (5:50)
Correlation vs Regression (1:43)
Geometrical Representation of the Linear Regression Model (1:25)
Python Packages Installation (4:39)
First Regression in Python (7:11)
First Regression in Python Exercise
Using Seaborn for Graphs (1:21)
How to Interpret the Regression Table (5:47)
Decomposition of Variability (3:37)
What is the OLS? (3:13)
R-Squared (5:30)
Multiple Linear Regression
Multiple Linear Regression (2:55)
Adjusted R-Squared (6:00)
Multiple Linear Regression Exercise
Test for Significance of the Model (F-Test) (2:01)
OLS Assumptions (2:21)
A1: Linearity (1:50)
A2: No Endogeneity (4:09)
A3: Normality and Homoscedasticity (5:47)
A4: No Autocorrelation (3:31)
A5: No Multicollinearity (3:26)
Dealing with Categorical Data - Dummy Variables (6:43)
Dealing with Categorical Data - Dummy Variables Exercise
Making Predictions with the Linear Regression (3:29)
Linear Regression with sklearn
What is sklearn? (2:14)
Game Plan for sklearn (1:55)
Simple Linear Regression with sklearn (5:38)
Simple Linear Regression with sklearn - Summary Table (4:48)
A Note on Normalization
Multiple Linear Regression with sklearn (3:10)
Adjusted R-Squared (4:45)
Adjusted R-Squared Exercise
Feature Selection through p-values (F-regression) (4:41)
A Note on Calculation of P-Values with sklearn
Creating a Summary Table with the p-values (2:10)
Multiple Linear Regression - Exercise
Feature Scaling (5:38)
Feature Selection through Standardization (5:22)
Making Predictions with Standardized Coefficients (3:52)
Feature Scaling - Exercise
Underfitting and Overfitting (2:42)
Training and Testing (6:54)
Linear Regression - Practical Example
Practical Example (Part 1) (11:59)
Practical Example (Part 2) (6:12)
A Note on Multicollinearity
Practical Example (Part 3) (3:15)
Dummies and VIF - Exercise
Practical Example (Part 4) (8:09)
Dummy Variables Interpretation - Exercise
Practical Example (Part 5) (7:34)
Linear Regression - Exercise
Logistic Regression
Introduction to Logistic Regression (1:19)
A Simple Example in Python (4:42)
Logistic vs Logit Function (4:00)
Building a Logistic Regression (2:48)
Bulding a Logistic Regression Exercise
An Invaluable Coding Tip (2:26)
Understanding Logistic Regression Tables (4:06)
Understanding Logistic Regression Tables - Exercise
What do the Odds Actually Mean (4:30)
Binary Predictors in a Logistic Regression (4:32)
Binary Predictors in a Logistic Regression - Exercise
Calculating the Accuracy of the Model (3:21)
Calculating the Accuracy of the Model - Exercise
Underfitting and Overfitting (3:43)
Testing the Model (5:05)
Testing the Model - Exercise
Cluster Analysis (Basics and Prerequisites)
Introduction to Cluster Analysis (3:41)
Some Examples of Clusters (4:31)
Difference between Classification and Clustering (2:32)
Math Prerequisites (3:19)
K-Means Clustering
K-Means Clustering (4:41)
A Simple Example of Clustering (7:48)
A Simple Example of Clustering - Exercise
Clustering Categorical Data (2:50)
Clustering Categorical Data - Exercise
How to Choose the Number of Clusters (6:11)
How to Choose the Number of Clusters - Exercise
Pros and Cons of K-Means Clustering (3:23)
To Standardize or to not Standardize (4:32)
Relationship between Clustering and Regression (1:31)
Market Segmentation with Cluster Analysis (Part 1) (6:03)
Market Segmentation with Cluster Analysis (Part 2) (6:58)
How is Clustering Useful? (4:47)
Exercise - Species Segmentation with Cluster Analysis (Part 1)
Exercise - Species Segmentation with Cluster Analysis (Part 2)
Other Types of Clustering
Types of Clustering (3:39)
Dendrogram (5:21)
Heatmaps (4:34)
Simple Linear Regression with sklearn - Summary Table
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