Machine Learning 101 with Scikit-learn and StatsModels

سرفصل های دوره

New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis

01 - Introduction

001 What Does the Course Cover

02 - Setting Up The Working Environment

001 Setting Up the Environment - An Introduction (Do Not Skip, Please)!

002 Why Python and Why Jupyter

003 Installing Anaconda

004 The Jupyter Dashboard - Part 1

005 The Jupyter Dashboard - Part 2

006 Jupyter Shortcuts.html

006 Shortcuts-for-Jupyter.pdf

007 Installing sklearn

008 Installing Packages - Exercise.html

009 Installing Packages - Solution.html

03 - Linear Regression with StatsModels

001 Course-notes-regression-analysis.pdf

001 Introduction to Regression Analysis

002 Course-notes-regression-analysis.pdf

002 The Linear Regression Model

003 Correlation vs Regression

004 Geometrical Representation

005 Python Packages Installation

006 Simple Linear Regression in Python

007 Simple Linear Regression in Python - Exercise.html

008 What is Seaborn

009 What Does the StatsModels Summary Regression Table Tell us

010 SST, SSR, and SSE

011 The Ordinary Least Squares (OLS)

012 Goodness of Fit The R-Squared

013 The Multiple Linear Regression Model

014 Adjusted R-Squared

015 Multiple Linear Regression - Exercise.html

016 F-Statistic and F-Test for a Linear Regression

017 Assumptions of the OLS Framework

018 A1 Linearity

019 A2 No Endogeneity

020 A3 Normality and Homoscedasticity

021 A4 No Autocorrelation

022 A5 No Multicollinearity

023 Dealing with Categorical Data

024 Dealing with Categorical Data - Exercise.html

025 Making Predictions

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04 - Linear Regression with Sklearn

001 What is sklearn

002 Game Plan for sklearn

003 Simple Linear Regression with sklearn

004 Simple Linear Regression with sklearn - Summary Table

005 A Note on Normalization.html

006 Simple Linear Regression with sklearn - Exercise.html

007 Multiple Linear Regression with sklearn

008 Adjusted R-Squared

009 Adjusted R-Squared - Exercise.html

010 Feature Selection through p-values (F-regression)

011 A Note on Calculation of P-values with sklearn.html

012 Creating a Summary Table with the p-values

013 Multiple Linear Regression - Exercise.html

014 Feature Scaling

015 Feature Selection through Standardization

016 Making Predictions with Standardized Coefficients

017 Feature Scaling - Exercise.html

018 Underfitting and Overfitting

019 Training and Testing

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05 - Linear Regression - Practical Example

001 Practical Example (Part 1)

002 Practical Example (Part 2)

003 A Note on Multicollinearity.html

004 Practical Example (Part 3)

005 Dummies and VIF - Exercise.html

006 Practical Example (Part 4)

007 Dummy Variables Interpretation - Exercise.html

008 Practical Example (Part 5)

009 Linear Regression - Exercise.html

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06 - Logistic Regression

001 Course-Notes-Logistic-Regression.pdf

001 Introduction to Logistic Regression

002 A Simple Example of a Logistic Regression in Python

002 Course-Notes-Logistic-Regression.pdf

003 What is the Difference Between a Logistic and a Logit Function

004 Your First Logistic Regression

005 Your First Logistic Regression - Exercise.html

006 A Coding Tip (optional)

007 Going through the Regression Summary Table

008 Going through the Regression Summary Table - Exercise.html

009 Interpreting the Odds Ratio

010 Dummies in a Logistic Regression

011 Dummies in a Logistic Regression - Exercise.html

012 Assessing the Accuracy of a Classification Model

013 Assessing the Accuracy of a Classification Model - Exercise.html

014 Underfitting and Overfitting

015 Testing our Model and Bulding a Confusion Matrix

016 Testing our Model and Bulding a Confusion Matrix - Exercise.html

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07 - Cluster Analysis

001 Course-Notes-Cluster-Analysis.pdf

001 Introduction to Cluster Analysis

002 Course-Notes-Cluster-Analysis.pdf

002 Examples of Clustering

003 Classification vs Clustering

004 Math Concepts Needed to Proceed

005 K-Means Clustering

006 A Hands on Example of K-Means

007 A Hands on Example of K-Means - Exercise.html

008 Categorical Data in Cluster Analysis

009 Categorical Data in Cluster Analysis - Exercise.html

010 The Elbow Method or How to Choose the Number of Clusters

011 The Elbow Method or How to Choose the Number of Clusters - Exercise.html

012 Pros and Cons of K-Means

013 Standardization of Features when Clustering

014 Cluster Analysis and Regression Analysis

015 Practical Example Market Segmentation (Part 1)

016 Practical Example Market Segmentation (Part 2)

017 What Can be Done with Cluster Analysis

018 EXERCISE Species Segmentation with Cluster Analysis (Part 1).html

019 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html

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08 - Cluster Analysis Additional Topics

001 Other Types of Clustering

002 The Dendrogram

003 Heatmaps

004 Completing 100%.html

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