365 Data Science - Advanced Statistical Methods in Python [CoursesGhar]
File List
- 4. Linear Regression - Practical Example/1. Practical Example (Part 1).mp4 84.8 MB
- 7. K-Means Clustering/10. How is Clustering Useful.mp4 69.2 MB
- 6. Cluster Analysis (Basics and Prerequisites)/2. Some Examples of Clusters.mp4 66.2 MB
- 3. Linear Regression with sklearn/1. What is sklearn.mp4 57.9 MB
- 5. Logistic Regression/3. Logistic vs Logit Function.mp4 56.0 MB
- 4. Linear Regression - Practical Example/5. Practical Example (Part 5).mp4 54.1 MB
- 1. Linear regression/3. The Linear Regression Model.mp4 49.5 MB
- 7. K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).mp4 47.1 MB
- 4. Linear Regression - Practical Example/4. Practical Example (Part 4).mp4 43.1 MB
- 2. Multiple Linear Regression/10. Dealing with Categorical Data - Dummy Variables.mp4 42.8 MB
- 7. K-Means Clustering/4. How to Choose the Number of Clusters.mp4 40.9 MB
- 7. K-Means Clustering/2. A Simple Example of Clustering.mp4 39.7 MB
- 1. Linear regression/12. R-Squared.mp4 39.3 MB
- 2. Multiple Linear Regression/2. Adjusted R-Squared.mp4 37.8 MB
- 8. Other Types of Clustering/2. Dendrogram.mp4 36.5 MB
- 4. Linear Regression - Practical Example/2. Practical Example (Part 2).mp4 36.3 MB
- 7. K-Means Clustering/6. To Standardize or to not Standardize.mp4 36.0 MB
- 3. Linear Regression with sklearn/13. Training and Testing.mp4 35.8 MB
- 6. Cluster Analysis (Basics and Prerequisites)/1. Introduction to Cluster Analysis.mp4 35.2 MB
- 1. Linear regression/1. Welcome to Advanced Statistics!.mp4 35.1 MB
- 8. Other Types of Clustering/1. Types of Clustering.mp4 34.4 MB
- 3. Linear Regression with sklearn/9. Feature Scaling.mp4 34.0 MB
- 1. Linear regression/7. First Regression in Python.mp4 32.6 MB
- 7. K-Means Clustering/5. Pros and Cons of K-Means Clustering.mp4 31.8 MB
- 1. Linear regression/9. How to Interpret the Regression Table.mp4 31.8 MB
- 7. K-Means Clustering/8. Market Segmentation with Cluster Analysis (Part 1).mp4 31.6 MB
- 2. Multiple Linear Regression/7. A3 - Normality and Homoscedasticity.mp4 31.3 MB
- 3. Linear Regression with sklearn/10. Feature Selection through Standardization.mp4 30.6 MB
- 7. K-Means Clustering/1. K-Means Clustering.mp4 29.9 MB
- 3. Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp4 29.7 MB
- 2. Multiple Linear Regression/6. A2 - No Endogeneity.mp4 29.7 MB
- 2. Multiple Linear Regression/8. A4 - No Autocorrelation.mp4 29.0 MB
- 1. Linear regression/10. Decomposition of Variability.mp4 28.6 MB
- 8. Other Types of Clustering/3. Heatmaps.mp4 28.3 MB
- 5. Logistic Regression/2. A Simple Example in Python.mp4 27.5 MB
- 6. Cluster Analysis (Basics and Prerequisites)/3. Difference between Classification and Clustering.mp4 26.9 MB
- 3. Linear Regression with sklearn/7. Feature Selection through p-values (F-regression).mp4 25.7 MB
- 5. Logistic Regression/11. Testing the Model.mp4 25.4 MB
- 3. Linear Regression with sklearn/4. Simple Linear Regression with sklearn - Summary Table.mp4 25.3 MB
- 5. Logistic Regression/8. Binary Predictors in a Logistic Regression.mp4 24.9 MB
- 5. Logistic Regression/10. Underfitting and Overfitting.mp4 24.9 MB
- 2. Multiple Linear Regression/1. Multiple Linear Regression.mp4 23.9 MB
- 3. Linear Regression with sklearn/6. Adjusted R-Squared.mp4 23.2 MB
- 5. Logistic Regression/9. Calculating the Accuracy of the Model.mp4 23.2 MB
- 5. Logistic Regression/5. An Invaluable Coding Tip.mp4 23.0 MB
- 1. Linear regression/11. What is the OLS.mp4 23.0 MB
- 2. Multiple Linear Regression/9. A5 - No Multicollinearity.mp4 22.6 MB
- 5. Logistic Regression/7. What do the Odds Actually Mean.mp4 21.7 MB
- 5. Logistic Regression/6. Understanding Logistic Regression Tables.mp4 20.9 MB
- 4. Linear Regression - Practical Example/3. Practical Example (Part 3).mp4 20.6 MB
- 5. Logistic Regression/1. Introduction to Logistic Regression.mp4 20.1 MB
- 3. Linear Regression with sklearn/11. Making Predictions with Standardized Coefficients.mp4 20.0 MB
- 3. Linear Regression with sklearn/12. Underfitting and Overfitting.mp4 18.4 MB
- 3. Linear Regression with sklearn/2. Game Plan for sklearn.mp4 18.1 MB
- 6. Cluster Analysis (Basics and Prerequisites)/4. Math Prerequisites.mp4 18.1 MB
- 2. Multiple Linear Regression/11. Making Predictions with the Linear Regression.mp4 17.7 MB
- 7. K-Means Clustering/3. Clustering Categorical Data.mp4 15.9 MB
- 2. Multiple Linear Regression/4. OLS Assumptions.mp4 15.6 MB
- 1. Linear regression/2. Introduction to Regression Analysis.mp4 15.3 MB
- 3. Linear Regression with sklearn/5. Multiple Linear Regression with sklearn.mp4 15.2 MB
- 1. Linear regression/6. Python Packages Installation.mp4 13.8 MB
- 5. Logistic Regression/4. Building a Logistic Regression.mp4 13.1 MB
- 1. Linear regression/4. Correlation vs Regression.mp4 12.1 MB
- 2. Multiple Linear Regression/3. Test for Significance of the Model (F-Test).mp4 10.7 MB
- 1. Linear regression/8. Using Seaborn for Graphs.mp4 10.7 MB
- 2. Multiple Linear Regression/5. A1 - Linearity.mp4 10.6 MB
- 7. K-Means Clustering/7. Relationship between Clustering and Regression.mp4 10.0 MB
- 3. Linear Regression with sklearn/8. Creating a Summary Table with the p-values.mp4 9.5 MB
- 1. Linear regression/5. Geometrical Representation of the Linear Regression Model.mp4 5.9 MB
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