1 What Does the Course Cover 2 Setting Up the Environment An Introduction Do Not Skip Please 3 Why Python and Why Jupyter 4 Installing Anaconda 5 The Jupyter Dashboard Part 1 6 The Jupyter Dashboard Part 2 7 Installing sklearn 8 Introduction to Regression Analysis 9 The Linear Regression Model 10 Correlation vs Regression 11 Geometrical Representation 12 Python Packages Installation 13 Simple Linear Regression in Python 14 What is Seaborn 15 What Does the StatsModels Summary Regression Table Tell us 16 SST SSR and SSE 17 The Ordinary Least Squares OLS 18 Goodness of Fit The RSquared 19 The Multiple Linear Regression Model 20 Adjusted RSquared 21 FStatistic and FTest for a Linear Regression 22 Assumptions of the OLS Framework 23 A1 Linearity 24 A2 No Endogeneity 25 A3 Normality and Homoscedasticity 26 A4 No Autocorrelation 27 A5 No Multicollinearity 28 Dealing with Categorical Data 29 Making Predictions 30 What is sklearn 31 Game Plan for sklearn 32 Simple Linear Regression with sklearn 33 Simple Linear Regression with sklearn Summary Table 34 Multiple Linear Regression with sklearn 35 Adjusted RSquared 36 Feature Selection through pvalues Fregression 37 Creating a Summary Table with the pvalues 38 Feature Scaling 39 Feature Selection through Standardization 40 Making Predictions with Standardized Coefficients 41 Underfitting and Overfitting 42 Training and Testing 43 Practical Example Part 1 44 Practical Example Part 2 45 Practical Example Part 3 46 Practical Example Part 4 47 Practical Example Part 5 48 Introduction to Logistic Regression 49 A Simple Example of a Logistic Regression in Python 50 What is the Difference Between a Logistic and a Logit Function 51 Your First Logistic Regression 52 A Coding Tip optional 53 Going through the Regression Summary Table 54 Interpreting the Odds Ratio 55 Dummies in a Logistic Regression 56 Assessing the Accuracy of a Classification Model 57 Underfitting and Overfitting 58 Testing our Model and Bulding a Confusion Matrix 59 Introduction to Cluster Analysis 60 Examples of Clustering 61 Classification vs Clustering 62 Math Concepts Needed to Proceed 63 KMeans Clustering 64 A Hands on Example of KMeans 65 Categorical Data in Cluster Analysis 66 The Elbow Method or How to Choose the Number of Clusters 67 Pros and Cons of KMeans 68 Standardization of Features when Clustering 69 Cluster Analysis and Regression Analysis 70 Practical Example Market Segmentation Part 1 71 Practical Example Market Segmentation Part 2 72 What Can be Done with Cluster Analysis 73 Other Types of Clustering 74 The Dendrogram 75 Heatmaps
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