文件列表829
- 12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4 218.66 MB
- 11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4 174.72 MB
- 13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4 153.3 MB
- 10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4 147.63 MB
- 10. Logistic Regression/6. How to analyze performance of a classification model.mp4 146.18 MB
- 13. Boosting Models/7. Introudction to Ensembling techniques.mp4 134.02 MB
- 20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.mp4 128.08 MB
- 2. Python for Data Analysis/17. Time Complexity.mp4 120.13 MB
- 2. Python for Data Analysis/21. Insertion and Selection Sort.mp4 120 MB
- 1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4 119.86 MB
- 18. Time Series Forecasting/8. Handling Missing Values.mp4 116.47 MB
- 2. Python for Data Analysis/22. Merge Sort.mp4 115.44 MB
- 17. Recommendation Engines/19. Introduction to SVD.mp4 112.02 MB
- 2. Python for Data Analysis/19. Binary Search.mp4 109.54 MB
- 9. Linear Regression/6. Analyzing the performance of Regression models.mp4 108.97 MB
- 11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4 108.17 MB
- 9. Linear Regression/9. Applying real time prediction on our model.mp4 107.61 MB
- 9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4 107.03 MB
- 10. Logistic Regression/1. Introduction to Logistic Regression.mp4 106.4 MB
- 5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4 106.25 MB
- 9. Linear Regression/5. Applying Cross Validation.mp4 105.62 MB
- 11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4 104.32 MB
- 20. Predicting Health Expense of Customers/2. Understanding the Dataset.mp4 104.05 MB
- 1. Python Fundamentals/3. Naming Convention for Variables.mp4 102.24 MB
- 16. Dimensionality Reduction/3. Solving a Real World Problem.mp4 98.82 MB
- 15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4 96.34 MB
- 19. Employee Promotion Prediction/2. Understanding the Dataset.mp4 95.88 MB
- 2. Python for Data Analysis/18. Linear Search.mp4 95.52 MB
- 20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.mp4 90.86 MB
- 13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4 90.82 MB
- 18. Time Series Forecasting/10. Time Series Decomposition.mp4 89.93 MB
- 20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.mp4 89.75 MB
- 15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4 88.49 MB
- 10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4 87.01 MB
- 20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.mp4 85.97 MB
- 9. Linear Regression/3. Feature Selection using RFECV.mp4 85.91 MB
- 18. Time Series Forecasting/3. Regression Vs Time Series.mp4 82.95 MB
- 5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp4 82.55 MB
- 12. Tree Based Models/1. Intuition for decision trees.mp4 81.99 MB
- 16. Dimensionality Reduction/18. Introduction to t-SNE.mp4 81.27 MB
- 9. Linear Regression/1. Introduction to Linear Regression.mp4 81.22 MB
- 17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4 80.86 MB
- 3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.mp4 79.87 MB
- 5. Data Cleaning/3. When should we delete the Missing values.mp4 79.62 MB
- 16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4 79.22 MB
- 18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp4 78.7 MB
- 1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4 78.04 MB
- 19. Employee Promotion Prediction/15. Performance Analysis.mp4 77.16 MB
- 1. Python Fundamentals/5. Scope of Variables.mp4 77.16 MB
- 18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp4 76.85 MB
