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Course content
Sections:
1
•
Activities:
0
•
Resources:
75
- 001 What Does the Course Cover NIIA gitir
- 002 Setting Up the Environment An Introduction Do Not Skip Please Vpf1 gitir
- 003 Why Python and Why Jupyter 7nBV gitir
- 005 Installing Anaconda gerP gitir
- 006 The Jupyter Dashboard Part 1 8VuU gitir
- 007 The Jupyter Dashboard Part 2 zPEE gitir
- 010 Installing sklearn NRGW gitir
- 013 Introduction to Regression Analysis W7rz gitir
- 015 The Linear Regression Model ZI8n gitir
- 017 Correlation vs Regression EDET gitir
- 019 Geometrical Representation wdSk gitir
- 021 Python Packages Installation nYAC gitir
- 022 Simple Linear Regression in Python DyeW gitir
- 024 What is Seaborn 2Wlh gitir
- 025 What Does the StatsModels Summary Regression Table Tell us kkRj gitir
- 027 SST SSR and SSE jkw8 gitir
- 029 The Ordinary Least Squares OLS KCv4 gitir
- 031 Goodness of Fit The R Squared nOHD gitir
- 033 The Multiple Linear Regression Model FpXJ gitir
- 035 Adjusted R Squared ZJqb gitir
- 038 F Statistic and F Test for a Linear Regression 6mb6 gitir
- 039 Assumptions of the OLS Framework O4i3 gitir
- 041 A1 Linearity MVN7 gitir
- 043 A2 No Endogeneity 7Wgz gitir
- 045 A3 Normality and Homoscedasticity uDMk gitir
- 046 A4 No Autocorrelation 7pkD gitir
- 048 A5 No Multicollinearity ENlq gitir
- 050 Dealing with Categorical Data 7VXt gitir
- 052 Making Predictions GICT gitir
- 053 What is sklearn seOB gitir
- 054 Game Plan for sklearn GeoY gitir
- 055 Simple Linear Regression with sklearn Ppec gitir
- 056 Simple Linear Regression with sklearn Summary Table UWWT gitir
- 059 Multiple Linear Regression with sklearn MKXQ gitir
- 060 Adjusted R Squared vzHB gitir
- 062 Feature Selection through p values F regression KCLE gitir
- 063 A Note on Calculation of P values with sklearn dnQF gitir
- 064 Creating a Summary Table with the p values bGZz gitir
- 067 Feature Selection through Standardization td2r gitir
- 068 Making Predictions with Standardized Coefficients 0xPq gitir
- 070 Underfitting and Overfitting H1qw gitir
- 071 Training and Testing MCsL gitir
- 072 Practical Example Part 1 rpzu gitir
- 073 Practical Example Part 2 dIGu gitir
- 075 Practical Example Part 3 TXaD gitir
- 077 Practical Example Part 4 iEar gitir
- 079 Practical Example Part 5 LhrF gitir
- 081 Introduction to Logistic Regression m3PV gitir
- 082 A Simple Example of a Logistic Regression in Python 83M3 gitir
- 083 What is the Difference Between a Logistic and a Logit Function f1pA gitir
- 084 Your First Logistic Regression MVFr gitir
- 086 A Coding Tip optional WCDl gitir
- 087 Going through the Regression Summary Table delz gitir
- 089 Interpreting the Odds Ratio Qm8U gitir
- 090 Dummies in a Logistic Regression ak1L gitir
- 092 Assessing the Accuracy of a Classification Model chMU gitir
- 094 Underfitting and Overfitting xOAe gitir
- 095 Testing our Model and Bulding a Confusion Matrix whuk gitir
- 097 Introduction to Cluster Analysis KNwP gitir
- 098 Examples of Clustering b0jZ gitir
- 099 Classification vs Clustering 950N gitir
- 100 Math Concepts Needed to Proceed aimG gitir
- 101 K Means Clustering gNih gitir
- 102 A Hands on Example of K Means 3O5Q gitir
- 104 Categorical Data in Cluster Analysis 2KAb gitir
- 106 The Elbow Method or How to Choose the Number of Clusters KkaT gitir
- 108 Pros and Cons of K Means CWe1 gitir
- 109 Standardization of Features when Clustering UcRJ gitir
- 110 Cluster Analysis and Regression Analysis UJ4d gitir
- 111 Practical Example Market Segmentation Part 1 3sfK gitir
- 112 Practical Example Market Segmentation Part 2 alYm gitir
- 113 What Can be Done with Cluster Analysis CGHh gitir
- 116 Other Types of Clustering tGAx gitir
- 117 The Dendrogram U6BG gitir
- 118 Heatmaps 6Fry gitir