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What Does the Course Cover?
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Setting Up the Environment - An Introduction (Do Not Skip, Please)!
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Why Python and Why Jupyter?
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Installing Anaconda
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The Jupyter Dashboard - Part 1
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The Jupyter Dashboard - Part 2
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Installing sklearn
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Introduction to Regression Analysis
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The Linear Regression Model
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Correlation vs Regression
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Geometrical Representation
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Python Packages Installation
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Simple Linear Regression in Python
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What is Seaborn?
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What Does the StatsModels Summary Regression Table Tell us?
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SST, SSR, and SSE
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The Ordinary Least Squares (OLS)
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Goodness of Fit: The R-Squared
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The Multiple Linear Regression Model
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F-Statistic and F-Test for a Linear Regression
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Assumptions of the OLS Framework
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A1: Linearity
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A2: No Endogeneity
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A3: Normality and Homoscedasticity
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A4: No Autocorrelation
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A5: No Multicollinearity
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Dealing with Categorical Data
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Making Predictions
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What is sklearn?
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Game Plan for sklearn
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Simple Linear Regression with sklearn
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Simple Linear Regression with sklearn - Summary Table
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Multiple Linear Regression with sklearn
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Feature Selection through p-values (F-regression)
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Creating a Summary Table with the p-values
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Feature Scaling
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Feature Selection through Standardization
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Making Predictions with Standardized Coefficients
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Underfitting and Overfitting
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Training and Testing
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Practical Example (Part 1)
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Practical Example (Part 2)
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Practical Example (Part 3)
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Practical Example (Part 4)
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Practical Example (Part 5)
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Introduction to Logistic Regression
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A Simple Example of a Logistic Regression in Python
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What is the Difference Between a Logistic and a Logit Function?
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A Coding Tip (optional)
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Going through the Regression Summary Table
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Interpreting the Odds Ratio
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Dummies in a Logistic Regression
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Assessing the Accuracy of a Classification Model
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Underfitting and Overfitting
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Testing our Model and Bulding a Confusion Matrix
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Introduction to Cluster Analysis
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Examples of Clustering
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Classification vs Clustering
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Math Concepts Needed to Proceed
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K-Means Clustering
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A Hands on Example of K-Means
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Categorical Data in Cluster Analysis
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The Elbow Method or How to Choose the Number of Clusters
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Pros and Cons of K-Means
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Standardization of Features when Clustering
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Cluster Analysis and Regression Analysis
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Practical Example: Market Segmentation (Part 1)
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Practical Example: Market Segmentation (Part 2)
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What Can be Done with Cluster Analysis?
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Other Types of Clustering
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The Dendrogram
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Heatmaps