GigaCourse Com Udemy Machine Learning in Python with Machine Learning Projects

GigaCourse Com Udemy Machine Learning in Python with Machine Learning Projects

Name	[GigaCourse.Com] Udemy - Machine Learning in Python with 5 Machine Learning Projects	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	22.4 GB
Total Files	829
Last Seen	2025-05-12 00:05
Hash	98F5357292CB5751E089A270D7C1E4570D0CF166

/0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/1. Python Fundamentals/
1. Why should you learn Python.mp4	68.9 MB
1. Why should you learn Python.srt	3.4 KB
10. Identity and Membership Operators.mp4	41.1 MB
10. Identity and Membership Operators.srt	2.9 KB
11. Quiz on Operators.html	0.1 KB
12. Quiz Solution.mp4	35.9 MB
12. Quiz Solution.srt	4.1 KB
13. String Formatting.mp4	53.8 MB
13. String Formatting.srt	4.9 KB
14. String Methods.mp4	45.4 MB
14. String Methods.srt	6.1 KB
15. User Input.mp4	43.0 MB
15. User Input.srt	2.9 KB
16. Quiz on Strings.html	0.1 KB
17. Quiz Solution.mp4	55.7 MB
17. Quiz Solution.srt	4.5 KB
18. If, elif, and else.mp4	69.1 MB
18. If, elif, and else.srt	3.8 KB
19. For and While.mp4	55.6 MB
19. For and While.srt	5.4 KB
2. Installing Python and Jupyter Notebook.mp4	35.1 MB
2. Installing Python and Jupyter Notebook.srt	2.5 KB
20. Break and Continue.mp4	42.7 MB
20. Break and Continue.srt	2.9 KB
21. Quiz on Loops and Conditionals.html	0.1 KB
22. Quiz Solution.mp4	51.4 MB
22. Quiz Solution.srt	4.4 KB
3. Naming Convention for Variables.mp4	107.2 MB
3. Naming Convention for Variables.srt	6.2 KB
4. Built in Data Types and Type Casting.mp4	125.7 MB
4. Built in Data Types and Type Casting.srt	6.7 KB
5. Scope of Variables.mp4	80.9 MB
5. Scope of Variables.srt	4.2 KB
6. Quiz on Variables and Data Types.html	0.1 KB
7. Quiz Solution.mp4	48.8 MB
7. Quiz Solution.srt	5.4 KB
8. Arithmetic and Assignment Operators.mp4	81.8 MB
8. Arithmetic and Assignment Operators.srt	8.2 KB
9. Comparison, Logical, and Bitwise Operators.mp4	65.4 MB
9. Comparison, Logical, and Bitwise Operators.srt	7.6 KB
/10. Logistic Regression/
1. Introduction to Logistic Regression.mp4	111.6 MB
1. Introduction to Logistic Regression.srt	7.0 KB
10. Industry Relevance of Logistic Regression.mp4	62.8 MB
10. Industry Relevance of Logistic Regression.srt	3.4 KB
11. Quiz on Modelling with Logistic Regression.html	0.1 KB
2. Implementing Logistic Regression using Sklearn.mp4	91.2 MB
2. Implementing Logistic Regression using Sklearn.srt	9.4 KB
3. Feature Selection using RFECV.mp4	44.2 MB
3. Feature Selection using RFECV.srt	3.1 KB
4. Hyperparameter tuning using Grid search.mp4	61.6 MB
4. Hyperparameter tuning using Grid search.srt	5.1 KB
5. Applying Cross Validation.mp4	59.5 MB
5. Applying Cross Validation.srt	3.9 KB
6. How to analyze performance of a classification model.mp4	153.3 MB
6. How to analyze performance of a classification model.srt	8.9 KB
7. Using accuracy score to analyze the performance of model.mp4	58.2 MB
7. Using accuracy score to analyze the performance of model.srt	4.8 KB
8. Using ROC-AUC score to analyze the performance of model.mp4	154.8 MB
8. Using ROC-AUC score to analyze the performance of model.srt	9.7 KB
9. Real time prediction using logistic regression.mp4	78.3 MB
9. Real time prediction using logistic regression.srt	7.2 KB
/11. Introduction to KNN, SVM, Naive Bayes/
1. Introduction to Support Vector machines.mp4	113.4 MB
1. Introduction to Support Vector machines.srt	5.9 KB
2. The kermel trick for support vector machine.mp4	73.8 MB
2. The kermel trick for support vector machine.srt	3.9 KB
3. Implementing support vector machine using sklearn.mp4	70.7 MB
3. Implementing support vector machine using sklearn.srt	7.6 KB
4. Introduction to K nearest neighbors.mp4	109.4 MB
4. Introduction to K nearest neighbors.srt	5.5 KB
5. Implementing KNN using Sklearn.mp4	34.8 MB
5. Implementing KNN using Sklearn.srt	2.1 KB
6. Introduction to Naive Bayes.mp4	183.2 MB
6. Introduction to Naive Bayes.srt	10.7 KB
7. Implementing Naive Bayes using sklearn.mp4	65.0 MB
7. Implementing Naive Bayes using sklearn.srt	3.5 KB
8. When should we apply SVM, KNN and Naive bayes.mp4	73.2 MB
8. When should we apply SVM, KNN and Naive bayes.srt	4.1 KB
9. Quiz on Other classification models.html	0.1 KB
/12. Tree Based Models/
1. Intuition for decision trees.mp4	86.0 MB
1. Intuition for decision trees.srt	4.7 KB
2. Attribute selection method- Gini Index and Entropy.mp4	229.3 MB
2. Attribute selection method- Gini Index and Entropy.srt	13.6 KB
3. Advantages and Issues with Decision trees.mp4	56.0 MB
3. Advantages and Issues with Decision trees.srt	3.0 KB
4. Implementing Decision tree using Sklearn.mp4	37.5 MB
4. Implementing Decision tree using Sklearn.srt	3.7 KB
5. Understanding the concept of Bagging.mp4	69.2 MB
5. Understanding the concept of Bagging.srt	3.7 KB
6. Introduction to Random forest.mp4	71.4 MB
6. Introduction to Random forest.srt	4.1 KB
7. Understanding the parameters of Random forest.mp4	56.3 MB
7. Understanding the parameters of Random forest.srt	4.4 KB
8. Implementing random forest using Sklearn.mp4	50.2 MB
8. Implementing random forest using Sklearn.srt	4.5 KB
9. Quiz on Tree based models.html	0.1 KB
/13. Boosting Models/
1. Understading the concept of boosting.mp4	59.9 MB
1. Understading the concept of boosting.srt	3.1 KB
2. Intuition for Adaboost and Gradient Boosting.mp4	160.7 MB
2. Intuition for Adaboost and Gradient Boosting.srt	8.9 KB
3. Implementing AdaBoost using sklearn.mp4	95.2 MB
3. Implementing AdaBoost using sklearn.srt	9.6 KB
4. Implementing Gradient Boosting using sklearn.mp4	70.2 MB
4. Implementing Gradient Boosting using sklearn.srt	4.9 KB
5. Getting High level intuition for XGBoost.mp4	43.1 MB
5. Getting High level intuition for XGBoost.srt	2.1 KB
6. Implementing XGBoost using sklearn.mp4	68.3 MB
6. Implementing XGBoost using sklearn.srt	4.8 KB
7. Introudction to Ensembling techniques.mp4	140.5 MB
7. Introudction to Ensembling techniques.srt	8.0 KB
8. Quiz on Boosting Models.html	0.1 KB
/14. Imbalanced Machine Learning/
1. Why Imbalanced Data needs extra attention.mp4	56.2 MB
1. Why Imbalanced Data needs extra attention.srt	3.3 KB
10. Implementing Synthetic Sampling using Imblearn.mp4	60.2 MB
10. Implementing Synthetic Sampling using Imblearn.srt	4.1 KB
11. Implementing Neighbors based Sampling using Imblearn.mp4	67.1 MB
11. Implementing Neighbors based Sampling using Imblearn.srt	4.7 KB
12. Combination of Oversampling and Under sampling.mp4	58.5 MB
12. Combination of Oversampling and Under sampling.srt	3.7 KB
13. Implementing Ensemble Models for Imbalanced Data.mp4	57.5 MB
13. Implementing Ensemble Models for Imbalanced Data.srt	3.8 KB
14. Introduction to XG Boost for Imbalanced Data.mp4	45.7 MB
14. Introduction to XG Boost for Imbalanced Data.srt	3.6 KB
15. Comparing the Results.mp4	43.5 MB
15. Comparing the Results.srt	2.2 KB
16. Quiz on Handling Imbalanced Datasets.html	0.1 KB
2. Using Resampling Techniques to Balance the Data.mp4	74.0 MB
2. Using Resampling Techniques to Balance the Data.srt	4.4 KB
3. Solving a Real World Problem.mp4	59.7 MB
3. Solving a Real World Problem.srt	4.3 KB
4. Preparing the Data for Predictive Modelling.mp4	60.7 MB
4. Preparing the Data for Predictive Modelling.srt	4.9 KB
5. Applying Logistic Regression using Sklearn.mp4	74.6 MB
5. Applying Logistic Regression using Sklearn.srt	5.2 KB
6. Applying Random Forest using Sklearn.mp4	44.7 MB
6. Applying Random Forest using Sklearn.srt	3.2 KB
7. Quiz on Introduction to Imbalanced Machine Learning.html	0.1 KB
8. Implementing Random Over Sampling using Imblearn.mp4	57.1 MB
8. Implementing Random Over Sampling using Imblearn.srt	4.3 KB
9. Implementing Random Under Sampling using Imblearn.mp4	60.3 MB
9. Implementing Random Under Sampling using Imblearn.srt	4.1 KB
/15. Introduction to Clustering Analysis/
1. Introduction to Clustering.mp4	60.6 MB
1. Introduction to Clustering.srt	3.2 KB
10. Clustering Multiple Dimensions.mp4	52.4 MB
10. Clustering Multiple Dimensions.srt	4.1 KB
11. Quiz on K Means Clustering.html	0.1 KB
12. Introduction to Hierarchal Clustering.mp4	92.8 MB
12. Introduction to Hierarchal Clustering.srt	4.9 KB
13. Introduction to Dendrograms.mp4	43.8 MB
13. Introduction to Dendrograms.srt	4.0 KB
14. Implementing Hierarchial Clustering.mp4	54.9 MB
14. Implementing Hierarchial Clustering.srt	3.7 KB
15. Introduction to DBSCAN Clustering.mp4	54.9 MB
15. Introduction to DBSCAN Clustering.srt	3.7 KB
16. Implementing DBSCAN Clustering.mp4	50.2 MB
16. Implementing DBSCAN Clustering.srt	3.7 KB
17. Quiz on Advanced Clustering Techniques.html	0.1 KB
2. Types of Clustering.mp4	68.4 MB
2. Types of Clustering.srt	4.0 KB
3. Applications of Clustering.mp4	58.7 MB
3. Applications of Clustering.srt	3.4 KB
4. Quiz on Introduction to Clustering.html	0.1 KB
5. Using the Elbow Method for Choosing the Best Value for K.mp4	70.3 MB
5. Using the Elbow Method for Choosing the Best Value for K.srt	3.7 KB
6. Introduction to K Means Clustering.mp4	51.7 MB
6. Introduction to K Means Clustering.srt	3.9 KB
7. Solving a Real World Problem.mp4	74.5 MB
7. Solving a Real World Problem.srt	5.1 KB
8. Implementing K Means on the Mall Dataset.mp4	75.0 MB
8. Implementing K Means on the Mall Dataset.srt	6.4 KB
9. Using Silhouette Score to analyze the clusters.mp4	101.0 MB
9. Using Silhouette Score to analyze the clusters.srt	7.1 KB
/16. Dimensionality Reduction/
1. Why High Dimensional Datasets are a Problem.mp4	83.1 MB
1. Why High Dimensional Datasets are a Problem.srt	4.4 KB
10. Quiz on Variance Filtering.html	0.1 KB
11. Introduction to Recursive Feature Selection.mp4	59.4 MB
11. Introduction to Recursive Feature Selection.srt	3.2 KB
12. Implementing Recursive Feature Selection.mp4	53.4 MB
12. Implementing Recursive Feature Selection.srt	4.3 KB
13. Introduction the Boruta Algorithm.mp4	55.0 MB
13. Introduction the Boruta Algorithm.srt	3.0 KB
14. Implementing the Boruta Algorithm.mp4	45.3 MB
14. Implementing the Boruta Algorithm.srt	4.6 KB
15. Quiz on Feature Selection.html	0.1 KB
16. Introduction to Principal Component Analysis.mp4	77.4 MB
16. Introduction to Principal Component Analysis.srt	4.2 KB
17. Implementing PCA.mp4	58.2 MB
17. Implementing PCA.srt	4.4 KB
18. Introduction to t-SNE.mp4	85.2 MB
18. Introduction to t-SNE.srt	4.6 KB
19. Implementing t-SNE.mp4	37.9 MB
19. Implementing t-SNE.srt	2.4 KB
2. Methods to solve the problem of High Dimensionality.mp4	59.9 MB
2. Methods to solve the problem of High Dimensionality.srt	3.4 KB
20. Introduction to Linear Discriminant Analysis.mp4	51.3 MB
20. Introduction to Linear Discriminant Analysis.srt	2.8 KB
21. Implementing LDA.mp4	38.5 MB
21. Implementing LDA.srt	2.8 KB
22. Difference between PCA, t-SNE, and LDA.mp4	67.9 MB
22. Difference between PCA, t-SNE, and LDA.srt	3.4 KB
23. Quiz on Machine Learning.html	0.1 KB
3. Solving a Real World Problem.mp4	103.6 MB
3. Solving a Real World Problem.srt	8.6 KB
4. Quiz on Introduction.html	0.1 KB
5. Introduction to Correlation using Heatmap.mp4	74.9 MB
5. Introduction to Correlation using Heatmap.srt	5.5 KB
6. Removing Highly Correlated Columns using Correlation.mp4	51.2 MB
6. Removing Highly Correlated Columns using Correlation.srt	4.1 KB
7. Quiz on Correlation Filtering.html	0.1 KB
8. Introduction to Variance Inflation Filtering.mp4	51.0 MB
8. Introduction to Variance Inflation Filtering.srt	2.4 KB
9. Implementing VIF using statsmodel.mp4	50.2 MB
9. Implementing VIF using statsmodel.srt	3.7 KB
[CourseClub.Me].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/17. Recommendation Engines/
1. Introduction to Recommender systems.mp4	42.5 MB
1. Introduction to Recommender systems.srt	2.2 KB
10. Quiz on Content Based Filtering.html	0.1 KB
11. Quiz Solution.mp4	50.9 MB
11. Quiz Solution.srt	4.5 KB
12. Introduction to Collaborative Filtering.mp4	84.8 MB
12. Introduction to Collaborative Filtering.srt	4.9 KB
13. Preprocessing the Data for Collaborative Filtering.mp4	75.9 MB
13. Preprocessing the Data for Collaborative Filtering.srt	5.8 KB
14. Implementation of User Based Collaborative Filtering.mp4	65.2 MB
14. Implementation of User Based Collaborative Filtering.srt	4.7 KB
15. Interpreting the Results obtained from User Based Filtering.mp4	66.7 MB
15. Interpreting the Results obtained from User Based Filtering.srt	5.7 KB
16. Implementation of Item Based Collaborative Filtering.mp4	66.6 MB
16. Implementation of Item Based Collaborative Filtering.srt	4.5 KB
17. Quiz on Collaborative Based Filtering.html	0.1 KB
18. Quiz Solution.mp4	58.3 MB
18. Quiz Solution.srt	4.7 KB
19. Introduction to SVD.mp4	117.5 MB
19. Introduction to SVD.srt	6.1 KB
2. What are it's Use Cases.mp4	47.2 MB
2. What are it's Use Cases.srt	2.5 KB
20. Implementing SVD using Surprise.mp4	42.6 MB
20. Implementing SVD using Surprise.srt	3.9 KB
21. Interpreting Results Obtained from SVD.mp4	48.2 MB
21. Interpreting Results Obtained from SVD.srt	5.5 KB
22. Comparing Content, and Collaborative Based Filtering.mp4	65.0 MB
22. Comparing Content, and Collaborative Based Filtering.srt	3.8 KB
23. Quiz on Singular Value Decomposition.html	0.1 KB
24. Quiz Solution.mp4	50.3 MB
24. Quiz Solution.srt	3.7 KB
25. Case Study for Netflix.mp4	59.1 MB
25. Case Study for Netflix.srt	3.0 KB
26. Case Study for Youtube.mp4	61.0 MB
26. Case Study for Youtube.srt	3.1 KB
3. Types of Recommender Systems.mp4	59.3 MB
3. Types of Recommender Systems.srt	3.5 KB
4. Evaluating Recommender Systems.mp4	55.7 MB
4. Evaluating Recommender Systems.srt	3.1 KB
5. Introduction to Content Based Filtering.mp4	61.9 MB
5. Introduction to Content Based Filtering.srt	3.7 KB
6. Preprocessing the Data for Content Based Filtering.mp4	80.4 MB
6. Preprocessing the Data for Content Based Filtering.srt	6.8 KB
7. Filtering Movies Based on Genres.mp4	61.6 MB
7. Filtering Movies Based on Genres.srt	6.0 KB
8. Introduction to Transactional Encoder.mp4	66.5 MB
8. Introduction to Transactional Encoder.srt	3.3 KB
9. Recommending Similar Movies to Watch.mp4	59.0 MB
9. Recommending Similar Movies to Watch.srt	4.1 KB
/18. Time Series Forecasting/
1. What is a Time Series Data.mp4	36.6 MB
1. What is a Time Series Data.srt	2.0 KB
10. Time Series Decomposition.mp4	94.3 MB
10. Time Series Decomposition.srt	7.8 KB
11. Splitting Time Series Data.mp4	66.6 MB
11. Splitting Time Series Data.srt	4.2 KB
12. Quiz on Time Series Analysis.html	0.1 KB
13. Basic Forecasting Techniques.mp4	58.2 MB
13. Basic Forecasting Techniques.srt	4.7 KB
14. Metrics for Time series Forecasting.mp4	82.5 MB
14. Metrics for Time series Forecasting.srt	4.9 KB
15. Simple Moving Averages.mp4	52.5 MB
15. Simple Moving Averages.srt	3.8 KB
16. Simple Exponential Smoothing.mp4	69.9 MB
16. Simple Exponential Smoothing.srt	4.1 KB
17. Holt and Holt Winter Exponential Smoothing.mp4	76.7 MB
17. Holt and Holt Winter Exponential Smoothing.srt	6.3 KB
18. Quiz on Smoothing Techniques.html	0.1 KB
19. Introduction to Auto Regressive Models.mp4	36.4 MB
19. Introduction to Auto Regressive Models.srt	2.0 KB
2. Types of Forecasting.mp4	47.5 MB
2. Types of Forecasting.srt	2.7 KB
20. Checking for Stationarity Part 1.mp4	68.2 MB
20. Checking for Stationarity Part 1.srt	3.4 KB
21. Checking for Stationarity using Statistical Methods Part 2.mp4	79.1 MB
21. Checking for Stationarity using Statistical Methods Part 2.srt	4.4 KB
22. Checking for Stationary Implementation.mp4	40.0 MB
22. Checking for Stationary Implementation.srt	3.2 KB
23. Converting Non-Stationary Series into Stationary.mp4	50.4 MB
23. Converting Non-Stationary Series into Stationary.srt	3.7 KB
24. Converting Non-Stationary Series into Stationary Implementation.mp4	50.5 MB
24. Converting Non-Stationary Series into Stationary Implementation.srt	4.0 KB
25. Auto Correlation and Partial Correlation.mp4	80.6 MB
25. Auto Correlation and Partial Correlation.srt	4.2 KB
26. Auto Correlation and Partial Correlation Implementation.mp4	40.3 MB
26. Auto Correlation and Partial Correlation Implementation.srt	3.7 KB
27. The Simple Auto Regressive Model.mp4	66.5 MB
27. The Simple Auto Regressive Model.srt	3.3 KB
28. The Simple Auto Regressive Model Implementation.mp4	68.1 MB
28. The Simple Auto Regressive Model Implementation.srt	6.7 KB
29. Moving Average Model.mp4	37.0 MB
29. Moving Average Model.srt	2.0 KB
3. Regression Vs Time Series.mp4	87.0 MB
3. Regression Vs Time Series.srt	4.6 KB
30. Moving Average Model Implementation.mp4	24.4 MB
30. Moving Average Model Implementation.srt	1.9 KB
31. Quiz on AR Models.html	0.1 KB
32. Understanding ARMA Model.mp4	59.5 MB
32. Understanding ARMA Model.srt	3.0 KB
33. Implementing ARMA Model.mp4	50.6 MB
33. Implementing ARMA Model.srt	3.2 KB
34. Understanding ARIMA Model.mp4	58.6 MB
34. Understanding ARIMA Model.srt	3.1 KB
35. Implementing ARIMA Model.mp4	34.8 MB
35. Implementing ARIMA Model.srt	2.3 KB
36. Understanding SARIMA Model.mp4	73.3 MB
36. Understanding SARIMA Model.srt	3.9 KB
37. Implementing SARIMA Model.mp4	40.0 MB
37. Implementing SARIMA Model.srt	2.7 KB
38. Quiz on Advanced AR Models.html	0.1 KB
39. Understanding ARIMAX Model.mp4	69.7 MB
39. Understanding ARIMAX Model.srt	3.6 KB
4. Applications of Time Series.mp4	49.6 MB
4. Applications of Time Series.srt	2.6 KB
40. Implementing ARIMAX Model.mp4	46.9 MB
40. Implementing ARIMAX Model.srt	3.4 KB
41. Understanding SARIMAX Model.mp4	46.0 MB
41. Understanding SARIMAX Model.srt	2.3 KB
42. Implementing SARIMAX Model.mp4	62.9 MB
42. Implementing SARIMAX Model.srt	3.6 KB
43. Quiz on ARIMAX and SARIMAX Models.html	0.1 KB
44. How to Choose the Right Model.mp4	36.9 MB
44. How to Choose the Right Model.srt	1.8 KB
45. Choosing the Right for Model Smaller Datasets.mp4	54.8 MB
45. Choosing the Right for Model Smaller Datasets.srt	3.0 KB
46. Choosing the Right Model for Larger Datasets.mp4	38.1 MB
46. Choosing the Right Model for Larger Datasets.srt	1.9 KB
47. Best Practices while Choosing a Time series Model..mp4	45.1 MB
47. Best Practices while Choosing a Time series Model..srt	2.5 KB
48. Quiz on Choosing the Right Model.html	0.1 KB
49. Why do we Evaluate Performance.mp4	33.3 MB
49. Why do we Evaluate Performance.srt	1.8 KB
5. Components of Time Series.mp4	54.5 MB
5. Components of Time Series.srt	3.0 KB
50. Mean Forecast Error.mp4	55.5 MB
50. Mean Forecast Error.srt	3.8 KB
51. Mean Absolute Error.mp4	37.3 MB
51. Mean Absolute Error.srt	2.5 KB
52. Mean Absolute Percentage Error.mp4	31.2 MB
52. Mean Absolute Percentage Error.srt	2.0 KB
53. Root Mean Squared Error.mp4	30.8 MB
53. Root Mean Squared Error.srt	2.0 KB
54. Quiz on Why do we Evaluate Performance.html	0.1 KB
6. Quiz on Introduction to Time Series.html	0.1 KB
7. Getting Time Series data.mp4	74.5 MB
7. Getting Time Series data.srt	6.6 KB
8. Handling Missing Values.mp4	122.1 MB
8. Handling Missing Values.srt	9.6 KB
9. Handling Outlier Values.mp4	67.6 MB
9. Handling Outlier Values.srt	4.6 KB
/19. Employee Promotion Prediction/
1. Setting up the Environment.mp4	43.7 MB
1. Setting up the Environment.srt	3.7 KB
10. Feature Engineering.mp4	52.9 MB
10. Feature Engineering.srt	3.4 KB
11. Categorical Encoding.mp4	39.3 MB
11. Categorical Encoding.srt	3.4 KB
12. Data Processing.mp4	70.9 MB
12. Data Processing.srt	5.1 KB
13. Feature Scaling.mp4	44.3 MB
13. Feature Scaling.srt	2.5 KB
14. Predictive Modelling.mp4	46.8 MB
14. Predictive Modelling.srt	3.1 KB
15. Performance Analysis.mp4	80.9 MB
15. Performance Analysis.srt	5.2 KB
16. Improvements Possible.mp4	43.9 MB
16. Improvements Possible.srt	2.5 KB
17. Major Takeaways from the Project.mp4	30.4 MB
17. Major Takeaways from the Project.srt	1.6 KB
18. Quiz on Employee Promotion Prediction.html	0.1 KB
2. Understanding the Dataset.mp4	100.5 MB
2. Understanding the Dataset.srt	6.5 KB
3. Understanding the Problem Statement.mp4	62.7 MB
3. Understanding the Problem Statement.srt	3.5 KB
4. Performing Descriptive Statistics.mp4	64.7 MB
4. Performing Descriptive Statistics.srt	4.5 KB
5. Missing Values Treatment.mp4	40.5 MB
5. Missing Values Treatment.srt	2.7 KB
6. Outlier Values Treatment.mp4	44.6 MB
6. Outlier Values Treatment.srt	4.0 KB
7. Univariate Analysis.mp4	55.7 MB
7. Univariate Analysis.srt	5.0 KB
8. Bivariate Analysis.mp4	39.0 MB
8. Bivariate Analysis.srt	3.3 KB
9. Multivariate Analysis.mp4	41.9 MB
9. Multivariate Analysis.srt	2.7 KB
/2. Python for Data Analysis/
1. Differences between Lists and Tuples.mp4	51.0 MB
1. Differences between Lists and Tuples.srt	3.5 KB
10. Quiz on Sets and Dictionaries.html	0.1 KB
11. Quiz Solution.mp4	40.1 MB
11. Quiz Solution.srt	5.3 KB
12. Introduction to Stacks and Queues.mp4	51.1 MB
12. Introduction to Stacks and Queues.srt	2.3 KB
13. Implementing Stacks and Queues using Lists.mp4	38.3 MB
13. Implementing Stacks and Queues using Lists.srt	2.8 KB
14. Implementing Stacks and Queues using Deque.mp4	43.6 MB
14. Implementing Stacks and Queues using Deque.srt	2.7 KB
15. Quiz on Stacks and Queues.html	0.1 KB
16. Quiz Solution.mp4	41.4 MB
16. Quiz Solution.srt	3.9 KB
17. Time Complexity.mp4	126.0 MB
17. Time Complexity.srt	6.4 KB
18. Linear Search.mp4	100.2 MB
18. Linear Search.srt	4.7 KB
19. Binary Search.mp4	114.9 MB
19. Binary Search.srt	5.7 KB
2. Operations on Lists.mp4	46.6 MB
2. Operations on Lists.srt	4.4 KB
20. Bubble Sort.mp4	79.2 MB
20. Bubble Sort.srt	4.0 KB
21. Insertion and Selection Sort.mp4	125.8 MB
21. Insertion and Selection Sort.srt	7.4 KB
22. Merge Sort.mp4	121.0 MB
22. Merge Sort.srt	6.4 KB
23. Quiz on Searching, Sorting, and Time Complexity.html	0.1 KB
24. Quiz Solution.mp4	76.8 MB
24. Quiz Solution.srt	7.7 KB
3. Operations on Tuples.mp4	28.8 MB
3. Operations on Tuples.srt	2.3 KB
4. Quiz on Lists and Tuples.html	0.1 KB
5. Quiz Solution.mp4	38.9 MB
5. Quiz Solution.srt	3.7 KB
6. Introduction to Dictionaries.mp4	70.1 MB
6. Introduction to Dictionaries.srt	4.7 KB
7. Nested Dictionaries.mp4	63.5 MB
7. Nested Dictionaries.srt	4.2 KB
8. Introduction to Sets.mp4	79.2 MB
8. Introduction to Sets.srt	4.6 KB
9. Set Operations.mp4	61.4 MB
9. Set Operations.srt	4.8 KB
/20. Predicting Health Expense of Customers/
1. Setting up the Environment.mp4	52.6 MB
1. Setting up the Environment.srt	3.7 KB
10. Applying Gradient Boosting Model.mp4	73.8 MB
10. Applying Gradient Boosting Model.srt	4.8 KB
11. Creating Ensembles of Models.mp4	59.8 MB
11. Creating Ensembles of Models.srt	4.6 KB
12. Comparing Performance of these Models.mp4	38.3 MB
12. Comparing Performance of these Models.srt	3.0 KB
13. More things to Try.mp4	51.1 MB
13. More things to Try.srt	2.8 KB
14. Major Takeaways from the Project.mp4	60.4 MB
14. Major Takeaways from the Project.srt	3.3 KB
15. Quiz on Predicting Health Expense of Customers.html	0.1 KB
2. Understanding the Dataset.mp4	109.1 MB
2. Understanding the Dataset.srt	6.8 KB
3. Understanding the Problem Statement.mp4	64.8 MB
3. Understanding the Problem Statement.srt	3.5 KB
4. Performing Univariate Analysis.mp4	94.1 MB
4. Performing Univariate Analysis.srt	6.8 KB
5. Performing Bivariate Analysis.mp4	74.9 MB
5. Performing Bivariate Analysis.srt	5.6 KB
6. Performing Multivariate Analysis.mp4	90.1 MB
6. Performing Multivariate Analysis.srt	7.1 KB
7. Preparing the data for Modelling.mp4	95.3 MB
7. Preparing the data for Modelling.srt	7.2 KB
8. Applying Linear Regression Model.mp4	134.3 MB
8. Applying Linear Regression Model.srt	8.3 KB
9. Applying Random Forest Model.mp4	57.0 MB
9. Applying Random Forest Model.srt	4.6 KB
/21. Determining Whether a Person should be Granted Loan/
1. Understanding the Problem Statement.mp4	47.7 MB
1. Understanding the Problem Statement.srt	3.5 KB
10. Applying Logistic Regression.mp4	54.9 MB
10. Applying Logistic Regression.srt	3.4 KB
11. Applying Gradient Boosting.mp4	40.5 MB
11. Applying Gradient Boosting.srt	3.0 KB
12. Summary.mp4	46.3 MB
12. Summary.srt	2.5 KB
13. Quiz on Determining Whether a Person should be Granted Loan.html	0.1 KB
2. Setting up the Environment.mp4	71.9 MB
2. Setting up the Environment.srt	4.9 KB
3. Understanding the Dataset.mp4	43.1 MB
3. Understanding the Dataset.srt	2.3 KB
4. Performing Descriptive Statistics.mp4	79.0 MB
4. Performing Descriptive Statistics.srt	6.2 KB
5. Data Cleaning.mp4	70.2 MB
5. Data Cleaning.srt	5.0 KB
6. Univariate Data Visualizations.mp4	68.3 MB
6. Univariate Data Visualizations.srt	4.4 KB
7. Bivariate Data Analysis.mp4	73.6 MB
7. Bivariate Data Analysis.srt	4.9 KB
8. Preparing the Data for Modelling.mp4	44.9 MB
8. Preparing the Data for Modelling.srt	3.1 KB
9. Applying Resampling.mp4	59.7 MB
9. Applying Resampling.srt	3.6 KB
/22. Optimizing Agricultural Production/
1. Setting up the Environment.mp4	48.7 MB
1. Setting up the Environment.srt	3.2 KB
10. Summarizing the Key-Points.mp4	42.4 MB
10. Summarizing the Key-Points.srt	2.3 KB
11. Quiz on Optimizing Agricultural Production.html	0.1 KB
2. Understanding the Dataset.mp4	57.9 MB
2. Understanding the Dataset.srt	3.3 KB
3. Understanding the Problem Statement.mp4	37.1 MB
3. Understanding the Problem Statement.srt	1.9 KB
4. Performing Descriptive Statistics.mp4	77.1 MB
4. Performing Descriptive Statistics.srt	6.4 KB
5. Analyzing Agricultural Conditions.mp4	41.1 MB
5. Analyzing Agricultural Conditions.srt	3.0 KB
6. Clustering Similar Crops.mp4	66.7 MB
6. Clustering Similar Crops.srt	4.2 KB
7. Visualizing the Hidden Patterns.mp4	29.1 MB
7. Visualizing the Hidden Patterns.srt	2.6 KB
8. Predictive Modelling.mp4	42.3 MB
8. Predictive Modelling.srt	3.3 KB
9. Real Time Predictions.mp4	29.0 MB
9. Real Time Predictions.srt	2.2 KB
/3. Python Functions Deep Dive/
1. Introduction to Functions.mp4	42.2 MB
1. Introduction to Functions.srt	2.6 KB
10. List, set, and Dictionary Comprehensions.mp4	57.2 MB
10. List, set, and Dictionary Comprehensions.srt	4.2 KB
11. Quiz on Anonymous Functions.html	0.1 KB
12. Quiz Solution.mp4	42.2 MB
12. Quiz Solution.srt	4.1 KB
13. Introduction to Aggregate Functions.mp4	32.1 MB
13. Introduction to Aggregate Functions.srt	2.6 KB
14. Introduction to Analytical Functions.mp4	36.4 MB
14. Introduction to Analytical Functions.srt	3.9 KB
15. Quiz on In Built Functions.html	0.1 KB
16. Quiz Solution.mp4	40.0 MB
16. Quiz Solution.srt	3.4 KB
17. Solving the Factorial Problem using Recursion.mp4	58.1 MB
17. Solving the Factorial Problem using Recursion.srt	3.2 KB
18. Solving the Fibonacci Problem using Recursion.mp4	65.7 MB
18. Solving the Fibonacci Problem using Recursion.srt	3.2 KB
19. Quiz on Recursions.html	0.1 KB
2. Default Parameters in Functions.mp4	56.6 MB
2. Default Parameters in Functions.srt	4.6 KB
20. Quiz Solution.mp4	39.9 MB
20. Quiz Solution.srt	4.1 KB
21. Introduction to Classes and Objects.mp4	41.5 MB
21. Introduction to Classes and Objects.srt	3.8 KB
22. Inheritance.mp4	34.1 MB
22. Inheritance.srt	2.7 KB
23. Encapsulation.mp4	65.2 MB
23. Encapsulation.srt	4.0 KB
24. Polymorphism.mp4	48.5 MB
24. Polymorphism.srt	2.7 KB
25. Quiz on Classes and Objects.html	0.1 KB
26. Quiz Solution.mp4	42.4 MB
26. Quiz Solution.srt	3.6 KB
3. Positional Arguments.mp4	33.7 MB
3. Positional Arguments.srt	2.3 KB
4. Keyword Arguments.mp4	38.0 MB
4. Keyword Arguments.srt	2.9 KB
5. Python Modules.mp4	44.8 MB
5. Python Modules.srt	3.0 KB
6. Quiz on Introduction to Functions.html	0.1 KB
7. Quiz Solution.mp4	50.0 MB
7. Quiz Solution.srt	5.0 KB
8. Lambda Functions.mp4	55.7 MB
8. Lambda Functions.srt	2.9 KB
9. Filter, Map, and Zip Functions.mp4	83.7 MB
9. Filter, Map, and Zip Functions.srt	6.9 KB
/4. Python for Data Science/
1. Introduction to datetime.mp4	39.3 MB
1. Introduction to datetime.srt	1.9 KB
10. Sets for Regular Expressions.mp4	58.9 MB
10. Sets for Regular Expressions.srt	3.9 KB
11. Quiz on Regular Expressions.html	0.1 KB
12. Quiz Solution.mp4	34.4 MB
12. Quiz Solution.srt	3.4 KB
13. Array Creation using Numpy.mp4	53.4 MB
13. Array Creation using Numpy.srt	3.3 KB
14. Mathematical Operations using Numpy.mp4	38.2 MB
14. Mathematical Operations using Numpy.srt	2.6 KB
15. Built-in Functions in Numpy.mp4	41.9 MB
15. Built-in Functions in Numpy.srt	2.8 KB
16. Quiz on Introduction to Numpy.html	0.1 KB
17. Quiz Solution.mp4	60.4 MB
17. Quiz Solution.srt	4.5 KB
18. Reading Datasets using Pandas.mp4	68.9 MB
18. Reading Datasets using Pandas.srt	3.8 KB
19. Plotting Data in Pandas.mp4	37.5 MB
19. Plotting Data in Pandas.srt	2.4 KB
2. The date and time class.mp4	35.2 MB
2. The date and time class.srt	3.0 KB
20. Indexing, Selecting, and Filtering Data using Pandas.mp4	72.3 MB
20. Indexing, Selecting, and Filtering Data using Pandas.srt	4.6 KB
21. Merging and Concatenating DataFrames.mp4	80.3 MB
21. Merging and Concatenating DataFrames.srt	5.1 KB
22. Lambda, Map, and Apply Functions.mp4	39.0 MB
22. Lambda, Map, and Apply Functions.srt	2.3 KB
23. Quiz on Introduction to Pandas.html	0.1 KB
24. Quiz Solution.mp4	57.4 MB
24. Quiz Solution.srt	4.6 KB
3. The datetime class.mp4	23.7 MB
3. The datetime class.srt	1.5 KB
4. The timedelta class.mp4	20.3 MB
4. The timedelta class.srt	1.2 KB
5. Quiz on Dates and Times.html	0.1 KB
6. Quiz Solution.mp4	46.2 MB
6. Quiz Solution.srt	3.9 KB
7. Meta Characters for Regular Expressions.mp4	77.6 MB
7. Meta Characters for Regular Expressions.srt	5.2 KB
8. Built-in Functions for Regular Expressions.mp4	39.4 MB
8. Built-in Functions for Regular Expressions.srt	2.6 KB
9. Special Characters for Regular Expressions.mp4	42.9 MB
9. Special Characters for Regular Expressions.srt	2.8 KB
/5. Data Cleaning/
1. Causes and Impact of Missing Values.mp4	67.5 MB
1. Causes and Impact of Missing Values.srt	3.7 KB
10. Finding out Outliers from the Data.mp4	66.3 MB
10. Finding out Outliers from the Data.srt	8.2 KB
11. Using Winsorization to deal with Outliers.mp4	53.0 MB
11. Using Winsorization to deal with Outliers.srt	2.9 KB
12. Deleting and Capping the Outliers.mp4	63.7 MB
12. Deleting and Capping the Outliers.srt	3.5 KB
13. Dealing with Outliers in a real-world scenario.mp4	53.4 MB
13. Dealing with Outliers in a real-world scenario.srt	3.9 KB
14. Quiz on Outliers Treatment.html	0.1 KB
15. Quiz Solution.mp4	58.8 MB
15. Quiz Solution.srt	4.5 KB
16. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp4	46.9 MB
16. Introduction to reindex, set_index, reset_index, and sort_index Functions.srt	4.7 KB
17. Introduction to Replace and Droplevel Function.mp4	34.6 MB
17. Introduction to Replace and Droplevel Function.srt	2.6 KB
18. Introduction to Split and Strip Function.mp4	39.7 MB
18. Introduction to Split and Strip Function.srt	4.0 KB
19. Introduction to Stack, and Unstack Functions.mp4	26.6 MB
19. Introduction to Stack, and Unstack Functions.srt	2.1 KB
2. Types of Missing Values.mp4	64.8 MB
2. Types of Missing Values.srt	3.4 KB
20. Introduction to Melt, Explode, and Squeeze Functions.mp4	43.4 MB
20. Introduction to Melt, Explode, and Squeeze Functions.srt	4.1 KB
21. Data Cleaning on Big Mart Dataset.mp4	40.2 MB
21. Data Cleaning on Big Mart Dataset.srt	4.1 KB
22. Data Cleaning on Movie Dataset.mp4	39.1 MB
22. Data Cleaning on Movie Dataset.srt	3.2 KB
23. Data Cleaning on Melbourne Housing Dataset.mp4	44.2 MB
23. Data Cleaning on Melbourne Housing Dataset.srt	4.0 KB
24. Data Cleaning on Naukri Dataset.mp4	111.4 MB
24. Data Cleaning on Naukri Dataset.srt	13.2 KB
3. When should we delete the Missing values.mp4	83.5 MB
3. When should we delete the Missing values.srt	4.3 KB
4. Imputing the Missing Values using the Business Logic.mp4	77.5 MB
4. Imputing the Missing Values using the Business Logic.srt	3.9 KB
5. Imputing Missing Values using MeanMedianMode.mp4	58.7 MB
5. Imputing Missing Values using MeanMedianMode.srt	3.0 KB
6. Imputing Missing Values in a real-time scenario.mp4	86.6 MB
6. Imputing Missing Values in a real-time scenario.srt	6.9 KB
7. Quiz on Missing Values Imputation.html	0.1 KB
8. Quiz Solution.mp4	51.5 MB
8. Quiz Solution.srt	4.9 KB
9. How Outliers can be harmful for Machine Learning Models.mp4	72.4 MB
9. How Outliers can be harmful for Machine Learning Models.srt	4.0 KB
[CourseClub.Me].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/6. Data Visualizations/
1. Univariate Analysis.mp4	59.8 MB
1. Univariate Analysis.srt	4.9 KB
10. Statistical Charts.mp4	40.2 MB
10. Statistical Charts.srt	5.0 KB
11. Polar Charts.mp4	30.7 MB
11. Polar Charts.srt	2.7 KB
12. Subplots.mp4	36.5 MB
12. Subplots.srt	2.9 KB
13. 3D Charts.mp4	25.8 MB
13. 3D Charts.srt	2.0 KB
14. Waffle Charts.mp4	30.8 MB
14. Waffle Charts.srt	2.0 KB
15. Maps.mp4	32.2 MB
15. Maps.srt	2.6 KB
16. Quiz on Advanced Visualizations.html	0.1 KB
17. Quiz Solution.mp4	51.2 MB
17. Quiz Solution.srt	4.3 KB
18. Animation with Bubbleplot.mp4	50.1 MB
18. Animation with Bubbleplot.srt	2.9 KB
19. Animation with Facets.mp4	28.0 MB
19. Animation with Facets.srt	1.8 KB
2. Bivariate Analysis.mp4	47.2 MB
2. Bivariate Analysis.srt	3.7 KB
20. Animation with Scatter Maps.mp4	23.8 MB
20. Animation with Scatter Maps.srt	1.6 KB
21. Animation with Choropleth Maps.mp4	32.1 MB
21. Animation with Choropleth Maps.srt	2.0 KB
22. Quiz on Animated Visualizations.html	0.1 KB
23. Quiz Solution.mp4	36.3 MB
23. Quiz Solution.srt	3.4 KB
24. Introduction to Ipywidgets.mp4	40.4 MB
24. Introduction to Ipywidgets.srt	3.6 KB
25. Interactive Univariate Analysis.mp4	31.3 MB
25. Interactive Univariate Analysis.srt	2.7 KB
26. Interactive Bivariate Analysis.mp4	35.5 MB
26. Interactive Bivariate Analysis.srt	3.6 KB
27. Interactive Multivariate Analysis.mp4	30.6 MB
27. Interactive Multivariate Analysis.srt	2.2 KB
28. Quiz on Interactive Visualizations.html	0.1 KB
29. Quiz Solution.mp4	56.4 MB
29. Quiz Solution.srt	4.5 KB
3. Multivariate Analysis.mp4	74.3 MB
3. Multivariate Analysis.srt	5.6 KB
30. Sunburst Charts.mp4	34.8 MB
30. Sunburst Charts.srt	2.6 KB
31. Parallel Co-ordinate Charts.mp4	24.1 MB
31. Parallel Co-ordinate Charts.srt	1.7 KB
32. Funnel Charts.mp4	41.0 MB
32. Funnel Charts.srt	3.2 KB
33. Gantt Charts.mp4	26.3 MB
33. Gantt Charts.srt	2.5 KB
34. Ternary Charts.mp4	21.4 MB
34. Ternary Charts.srt	1.8 KB
35. Tree Maps.mp4	22.5 MB
35. Tree Maps.srt	1.8 KB
36. Network Charts.mp4	41.7 MB
36. Network Charts.srt	2.7 KB
37. Quiz on Miscellaneous Charts.html	0.1 KB
38. Quiz Solution.mp4	40.4 MB
38. Quiz Solution.srt	4.2 KB
4. Quiz on Basics of Visualization.html	0.1 KB
5. Quiz Solution.mp4	49.4 MB
5. Quiz Solution.srt	4.1 KB
6. Scatter Plots.mp4	47.4 MB
6. Scatter Plots.srt	4.4 KB
7. Charts with Colorscale.mp4	33.4 MB
7. Charts with Colorscale.srt	2.5 KB
8. Bar, Line, and Area Charts.mp4	50.9 MB
8. Bar, Line, and Area Charts.srt	5.0 KB
9. Facet Grids.mp4	39.8 MB
9. Facet Grids.srt	2.7 KB
/7. Feature Engineering/
1. Introduction to Feature Engineering.mp4	63.0 MB
1. Introduction to Feature Engineering.srt	3.6 KB
10. Finding the Words, Characters, and Punctuation Count.mp4	38.1 MB
10. Finding the Words, Characters, and Punctuation Count.srt	4.2 KB
11. Counting Nouns and Verbs in the Text.mp4	32.9 MB
11. Counting Nouns and Verbs in the Text.srt	2.7 KB
12. Counting Adjectives, Adverb, and Pronouns.mp4	24.9 MB
12. Counting Adjectives, Adverb, and Pronouns.srt	2.1 KB
13. Introduction to Assign and Update Functions.mp4	37.9 MB
13. Introduction to Assign and Update Functions.srt	3.3 KB
14. Introduction to at_time and between_time Functions.mp4	31.7 MB
14. Introduction to at_time and between_time Functions.srt	3.0 KB
15. Introduction to nlargest and nsmallest Functions.mp4	37.0 MB
15. Introduction to nlargest and nsmallest Functions.srt	3.6 KB
16. Introduction to Expanding Function.mp4	29.8 MB
16. Introduction to Expanding Function.srt	2.4 KB
17. Introduction to Cumulative Functions.mp4	32.6 MB
17. Introduction to Cumulative Functions.srt	3.2 KB
18. Quiz on Feature Engineering Functions.html	0.1 KB
19. Quiz Solution.mp4	53.7 MB
19. Quiz Solution.srt	4.0 KB
2. Removing Unnecessary Columns.mp4	59.6 MB
2. Removing Unnecessary Columns.srt	4.4 KB
20. Feature Engineering on Employee Data.mp4	59.9 MB
20. Feature Engineering on Employee Data.srt	6.2 KB
21. Feature Engineering on FIFA Data.mp4	46.9 MB
21. Feature Engineering on FIFA Data.srt	4.5 KB
22. Feature Engineering on Hotel Reviews.mp4	36.8 MB
22. Feature Engineering on Hotel Reviews.srt	3.3 KB
23. Feature Engineering on Marketing Data.mp4	61.4 MB
23. Feature Engineering on Marketing Data.srt	6.5 KB
24. Feature Engineering on Titanic Data.mp4	52.0 MB
24. Feature Engineering on Titanic Data.srt	5.4 KB
25. Quiz on Feature Engineering on Real World Datasets.html	0.1 KB
26. Quiz Solution.mp4	68.0 MB
26. Quiz Solution.srt	4.6 KB
3. Decomposing Time and Date Features.mp4	40.2 MB
3. Decomposing Time and Date Features.srt	2.4 KB
4. Decomposing Categorical Features.mp4	40.1 MB
4. Decomposing Categorical Features.srt	2.8 KB
5. Binning Numerical Features.mp4	62.2 MB
5. Binning Numerical Features.srt	4.6 KB
6. Aggregating Features.mp4	59.6 MB
6. Aggregating Features.srt	4.0 KB
7. Introduction to Feature Engineering on Text Data.mp4	35.5 MB
7. Introduction to Feature Engineering on Text Data.srt	1.8 KB
8. Reading and Summarizing the Text.mp4	32.0 MB
8. Reading and Summarizing the Text.srt	3.2 KB
9. Finding the Length, Polarity and Subjectivity.mp4	76.6 MB
9. Finding the Length, Polarity and Subjectivity.srt	5.9 KB
/8. Data Processing/
1. Types of Encoding Techniques.mp4	63.9 MB
1. Types of Encoding Techniques.srt	4.2 KB
10. Log transformation.mp4	29.4 MB
10. Log transformation.srt	2.6 KB
11. BoxCox transformation.mp4	34.1 MB
11. BoxCox transformation.srt	2.5 KB
12. Quiz on Data Transformation.html	0.1 KB
13. Train, Test and Validation Split.mp4	46.4 MB
13. Train, Test and Validation Split.srt	3.7 KB
14. Standardization and Normalization.mp4	41.6 MB
14. Standardization and Normalization.srt	2.5 KB
15. Quiz on Data Splitting and Feature Scaling.html	0.1 KB
2. Label Encoding.mp4	35.2 MB
2. Label Encoding.srt	3.0 KB
3. Feature Mapping for Ordinal Variables.mp4	30.4 MB
3. Feature Mapping for Ordinal Variables.srt	2.3 KB
4. OneHot Encoding.mp4	36.3 MB
4. OneHot Encoding.srt	3.0 KB
5. Binary and BaseN Encoding.mp4	34.8 MB
5. Binary and BaseN Encoding.srt	3.5 KB
6. Mean and Frequency Encoding.mp4	23.9 MB
6. Mean and Frequency Encoding.srt	2.2 KB
7. Quiz on Dealing with Categorical data.html	0.1 KB
8. Introduction to Skewness and Normal Distribution.mp4	39.4 MB
8. Introduction to Skewness and Normal Distribution.srt	2.9 KB
9. Square and Cube Root Transformation.mp4	41.3 MB
9. Square and Cube Root Transformation.srt	3.3 KB
/9. Linear Regression/
1. Introduction to Linear Regression.mp4	85.2 MB
1. Introduction to Linear Regression.srt	4.8 KB
10. Industry relevance of linear regression.mp4	52.3 MB
10. Industry relevance of linear regression.srt	2.8 KB
11. Quiz on Modelling with Linear Regression.html	0.1 KB
2. Implementing Linear Regression using Sklearn.mp4	77.0 MB
2. Implementing Linear Regression using Sklearn.srt	10.9 KB
3. Feature Selection using RFECV.mp4	90.1 MB
3. Feature Selection using RFECV.srt	6.4 KB
4. Data Transformation with Linear Regression.mp4	60.3 MB
4. Data Transformation with Linear Regression.srt	7.0 KB
5. Applying Cross Validation.mp4	110.8 MB
5. Applying Cross Validation.srt	5.4 KB
6. Analyzing the performance of Regression models.mp4	114.3 MB
6. Analyzing the performance of Regression models.srt	5.1 KB
7. R2 score and adjuted R2 score intuition.mp4	112.2 MB
7. R2 score and adjuted R2 score intuition.srt	6.0 KB
8. MAE, RMSE, R2 and Adjusted R2 in code.mp4	51.4 MB
8. MAE, RMSE, R2 and Adjusted R2 in code.srt	5.6 KB
9. Applying real time prediction on our model.mp4	112.8 MB
9. Applying real time prediction on our model.srt	9.9 KB
/
[CourseClub.Me].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
Total files 829