[FreeCourseSite.com] Udemy - Machine Learning using Python

Creation Time	March 27, 2025, 4:41 a.m.
Last Access Time	May 11, 2026, 2:26 p.m.
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21. Time Series - Preprocessing in Pyhton/3. Time Series - Visualization in Python.mp4 208.2 MB
5. Linear Regression/16. Ridge regression and Lasso in Python.mp4 174.9 MB
21. Time Series - Preprocessing in Pyhton/5. Time Series - Feature Engineering in Python.mp4 142.5 MB
22. Time Series - Important Concepts/5. Differencing in Python.mp4 141.2 MB
21. Time Series - Preprocessing in Pyhton/1. Data Loading in Python.mp4 134.6 MB
21. Time Series - Preprocessing in Pyhton/7. Time Series - Upsampling and Downsampling in Python.mp4 124.3 MB
3. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 113.3 MB
5. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 103.2 MB
4. Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4 100.4 MB
12. Simple Classification Tree/4. Classification tree in Python Training.mp4 99.6 MB
4. Data Preprocessing/8. Outlier Treatment in Python.mp4 98.3 MB
13. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4 97.1 MB
14. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4 91.7 MB
5. Linear Regression/9. Multiple Linear Regression in Python.mp4 85.1 MB
24. Time Series - ARIMA model/3. ARIMA model in Python.mp4 84.9 MB
5. Linear Regression/5. Simple Linear Regression in Python.mp4 84.9 MB
21. Time Series - Preprocessing in Pyhton/2. Time Series - Visualization Basics.mp4 80.3 MB
5. Linear Regression/14. Subset selection techniques.mp4 79.1 MB
20. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4 78.9 MB
22. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4 78.6 MB
4. Data Preprocessing/6. EDD in Python.mp4 78.5 MB
23. Time Series - Implementation in Python/1. Test Train Split in Python.mp4 77.1 MB
21. Time Series - Preprocessing in Pyhton/4. Time Series - Feature Engineering Basics.mp4 76.9 MB
4. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 76.3 MB
2. Basics of statistics/3. Describing data Graphically.mp4 76.0 MB
9. K Nearest neighbors classifier/3. K-Nearest Neighbors classifier.mp4 75.4 MB
25. Time Series - SARIMA model/2. SARIMA model in Python.mp4 75.1 MB
15. Ensemble technique 3 - Boosting/4. Ensemble technique 3c - XGBoost in Python.mp4 75.0 MB
4. Data Preprocessing/17. Correlation Analysis.mp4 74.7 MB
19. Creating Support Vector Machine Model in Python/4. SVM based Regression Model in Python.mp4 73.7 MB
17. Support Vector classifiers/1. Support Vector classifiers.mp4 73.7 MB
19. Creating Support Vector Machine Model in Python/7. SVM Based classification model.mp4 72.9 MB
7. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4 69.5 MB
1. Setting up Python and Jupyter notebook/3. Opening Jupyter Notebook.mp4 68.4 MB
1. Setting up Python and Jupyter notebook/6. Strings in Python Python Basics.mp4 68.2 MB
23. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4 67.6 MB
19. Creating Support Vector Machine Model in Python/8. Hyper Parameter Tuning.mp4 67.5 MB
4. Data Preprocessing/13. Variable transformation and deletion in Python.mp4 67.3 MB
4. Data Preprocessing/18. Correlation Analysis in Python.mp4 65.6 MB
23. Time Series - Implementation in Python/7. Moving Average model in Python.mp4 64.3 MB
5. Linear Regression/12. Test train split in Python.mp4 64.0 MB
1. Setting up Python and Jupyter notebook/7. Lists, Tuples and Directories Python Basics.mp4 63.2 MB
23. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4 61.8 MB
11. Simple Decision Trees/3. Understanding a Regression Tree.mp4 61.1 MB
7. Logistic Regression/7. Creating Confusion Matrix in Python.mp4 60.8 MB
9. K Nearest neighbors classifier/2. Test-Train Split in Python.mp4 59.0 MB
11. Simple Decision Trees/2. Basics of Decision Trees.mp4 58.7 MB
23. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4 56.9 MB
14. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4 54.9 MB
19. Creating Support Vector Machine Model in Python/5. Classification model - Preprocessing.mp4 53.9 MB
12. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4 53.8 MB
5. Linear Regression/7. The F - statistic.mp4 53.8 MB
18. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4 53.3 MB
9. K Nearest neighbors classifier/5. K-Nearest Neighbors in Python Part 2.mp4 53.2 MB
24. Time Series - ARIMA model/1. ACF and PACF.mp4 52.8 MB
6. Introduction to the classification Models/1. Three classification models and Data set.mp4 52.2 MB
1. Setting up Python and Jupyter notebook/9. Working with Pandas Library of Python.mp4 50.7 MB
21. Time Series - Preprocessing in Pyhton/9. Moving Average.mp4 50.0 MB
1. Setting up Python and Jupyter notebook/8. Working with Numpy Library of Python.mp4 46.4 MB
9. K Nearest neighbors classifier/4. K-Nearest Neighbors in Python Part 1.mp4 46.2 MB
20. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp4 45.9 MB
5. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 45.0 MB
11. Simple Decision Trees/1. Introduction to Decision trees.mp4 44.9 MB
19. Creating Support Vector Machine Model in Python/10. Radial Kernel with Hyperparameter Tuning.mp4 44.5 MB
1. Setting up Python and Jupyter notebook/4. Introduction to Jupyter.mp4 44.1 MB
22. Time Series - Important Concepts/4. Differencing.mp4 44.0 MB
2. Basics of statistics/4. Measures of Centers.mp4 43.3 MB
5. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 42.5 MB
19. Creating Support Vector Machine Model in Python/3. Standardizing the data.mp4 42.1 MB
5. Linear Regression/10. Test-train split.mp4 41.8 MB
10. Comparing results from 3 models/1. Understanding the results of classification models.mp4 41.7 MB
3. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4 41.0 MB
8. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4 40.9 MB
15. Ensemble technique 3 - Boosting/1. Boosting.mp4 40.9 MB
4. Data Preprocessing/16. Dummy variable creation in Python.mp4 40.8 MB
16. Support Vector Machines/2. The Concept of a Hyperplane.mp4 40.5 MB
4. Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp4 40.5 MB
12. Simple Classification Tree/1. Classification tree.mp4 40.2 MB
25. Time Series - SARIMA model/1. SARIMA model.mp4 40.2 MB
15. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4 39.9 MB
1. Setting up Python and Jupyter notebook/10. Working with Seaborn Library of Python.mp4 39.6 MB
13. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4 39.3 MB
9. K Nearest neighbors classifier/1. Test-Train Split.mp4 39.3 MB
5. Linear Regression/6. Multiple Linear Regression.mp4 38.2 MB
24. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4 36.1 MB
7. Logistic Regression/8. Evaluating performance of model.mp4 35.2 MB
7. Logistic Regression/5. Training multiple predictor Logistic model in Python.mp4 34.3 MB
4. Data Preprocessing/4. Importing Data in Python.mp4 33.9 MB
4. Data Preprocessing/10. Missing Value Imputation in Python.mp4 33.3 MB
5. Linear Regression/15. Shrinkage methods Ridge and Lasso.mp4 33.3 MB
7. Logistic Regression/1. Logistic Regression.mp4 32.9 MB
23. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4 31.7 MB
20. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4 31.4 MB
16. Support Vector Machines/3. Maximum Margin Classifier.mp4 30.6 MB
15. Ensemble technique 3 - Boosting/3. Ensemble technique 3b - AdaBoost in Python.mp4 30.5 MB
4. Data Preprocessing/5. Univariate analysis and EDD.mp4 29.3 MB
4. Data Preprocessing/2. Data Exploration.mp4 28.4 MB
22. Time Series - Important Concepts/2. Random Walk.mp4 28.0 MB
11. Simple Decision Trees/12. Plotting decision tree in Python.mp4 27.0 MB
7. Logistic Regression/3. Result of Simple Logistic Regression.mp4 26.9 MB
4. Data Preprocessing/7. Outlier Treatment.mp4 26.6 MB
24. Time Series - ARIMA model/2. ARIMA model - Basics.mp4 26.5 MB
19. Creating Support Vector Machine Model in Python/2. Importing and preprocessing data in Python.mp4 26.5 MB
19. Creating Support Vector Machine Model in Python/9. Polynomial Kernel with Hyperparameter Tuning.mp4 26.5 MB
2. Basics of statistics/5. Measures of Dispersion.mp4 26.3 MB
14. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4 26.0 MB
11. Simple Decision Trees/9. Test-Train split in Python.mp4 25.6 MB
5. Linear Regression/11. Bias Variance trade-off.mp4 25.1 MB
11. Simple Decision Trees/14. Pruning a tree in Python.mp4 25.1 MB
11. Simple Decision Trees/13. Pruning a tree.mp4 25.0 MB
11. Simple Decision Trees/7. Dummy Variable Creation in Python.mp4 24.6 MB
4. Data Preprocessing/9. Missing Value Imputation.mp4 24.5 MB
21. Time Series - Preprocessing in Pyhton/6. Time Series - Upsampling and Downsampling.mp4 23.3 MB
2. Basics of statistics/1. Types of Data.mp4 23.3 MB
10. Comparing results from 3 models/2. Summary of the three models.mp4 22.2 MB
16. Support Vector Machines/1. Introduction to SVM's.mp4 21.6 MB
5. Linear Regression/8. Interpreting results of Categorical variables.mp4 21.4 MB
11. Simple Decision Trees/10. Creating Decision tree in Python.mp4 21.3 MB
7. Logistic Regression/6. Confusion Matrix.mp4 21.1 MB
23. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4 20.9 MB
12. Simple Classification Tree/2. The Data set for Classification problem.mp4 20.9 MB
1. Setting up Python and Jupyter notebook/2. This is a Milestone!.mp4 20.7 MB
4. Data Preprocessing/14. Non-usable variables.mp4 20.2 MB
11. Simple Decision Trees/4. The stopping criteria for controlling tree growth.mp4 19.4 MB
21. Time Series - Preprocessing in Pyhton/8. Time Series - Power Transformation.mp4 18.7 MB
20. Time Series Analysis and Forecasting/1. Introduction.mp4 18.7 MB
11. Simple Decision Trees/11. Evaluating model performance in Python.mp4 18.3 MB
1. Setting up Python and Jupyter notebook/1. Installing Python and Anaconda.mp4 18.1 MB
8. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4 17.6 MB
4. Data Preprocessing/1. Gathering Business Knowledge.mp4 17.3 MB
6. Introduction to the classification Models/3. The problem statements.mp4 17.1 MB
4. Data Preprocessing/11. Seasonality in Data.mp4 17.0 MB
6. Introduction to the classification Models/4. Why can't we use Linear Regression.mp4 16.9 MB
11. Simple Decision Trees/8. Dependent- Independent Data split in Python.mp4 16.9 MB
5. Linear Regression/13. Regression models other than OLS.mp4 16.5 MB
11. Simple Decision Trees/5. Importing the Data set into Python.mp4 15.9 MB
17. Support Vector classifiers/2. Limitations of Support Vector Classifiers.mp4 15.6 MB
22. Time Series - Important Concepts/1. White Noise.mp4 14.7 MB
16. Support Vector Machines/4. Limitations of Maximum Margin Classifier.mp4 14.5 MB
5. Linear Regression/17. Heteroscedasticity.mp4 14.5 MB
1. Setting up Python and Jupyter notebook/5. Arithmetic operators in Python Python Basics.mp4 13.5 MB
7. Logistic Regression/9. Evaluating model performance in Python.mp4 13.4 MB
11. Simple Decision Trees/6. Missing value treatment in Python.mp4 12.9 MB
20. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4 12.1 MB
2. Basics of statistics/2. Types of Statistics.mp4 12.0 MB
25. Time Series - SARIMA model/4. The final milestone!.mp4 11.9 MB
21. Time Series - Preprocessing in Pyhton/10. Exponential Smoothing.mp4 10.9 MB
19. Creating Support Vector Machine Model in Python/6. Classification model - Standardizing the data.mp4 10.6 MB
5. Linear Regression/1. The Problem Statement.mp4 10.1 MB
12. Simple Classification Tree/5. Advantages and Disadvantages of Decision Trees.mp4 10.1 MB
7. Logistic Regression/4. Logistic with multiple predictors.mp4 8.5 MB
6. Introduction to the classification Models/2. Importing the data into Python.mp4 6.9 MB
25. Time Series - SARIMA model/3. Stationary time Series.mp4 5.7 MB
19. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4 4.7 MB
1. Setting up Python and Jupyter notebook/7. Lists, Tuples and Directories Python Basics.srt 22.2 KB
5. Linear Regression/16. Ridge regression and Lasso in Python.srt 21.5 KB
4. Data Preprocessing/12. Bi-variate analysis and Variable transformation.srt 20.2 KB
5. Linear Regression/3. Assessing accuracy of predicted coefficients.srt 19.9 KB
3. Introduction to Machine Learning/1. Introduction to Machine Learning.srt 19.4 KB
1. Setting up Python and Jupyter notebook/6. Strings in Python Python Basics.srt 18.6 KB
1. Setting up Python and Jupyter notebook/4. Introduction to Jupyter.srt 15.5 KB
5. Linear Regression/14. Subset selection techniques.srt 15.3 KB
4. Data Preprocessing/8. Outlier Treatment in Python.srt 14.5 KB
5. Linear Regression/9. Multiple Linear Regression in Python.srt 14.4 KB
5. Linear Regression/5. Simple Linear Regression in Python.srt 13.4 KB
2. Basics of statistics/3. Describing data Graphically.srt 13.2 KB
1. Setting up Python and Jupyter notebook/8. Working with Numpy Library of Python.srt 12.8 KB
5. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 12.7 KB
5. Linear Regression/10. Test-train split.srt 12.6 KB
4. Data Preprocessing/6. EDD in Python.srt 11.8 KB
4. Data Preprocessing/17. Correlation Analysis.srt 11.8 KB
5. Linear Regression/7. The F - statistic.srt 11.5 KB
1. Setting up Python and Jupyter notebook/9. Working with Pandas Library of Python.srt 10.3 KB
3. Introduction to Machine Learning/2. Building a Machine Learning Model.srt 10.2 KB
1. Setting up Python and Jupyter notebook/3. Opening Jupyter Notebook.srt 10.1 KB
5. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 9.8 KB
5. Linear Regression/15. Shrinkage methods Ridge and Lasso.srt 9.4 KB
4. Data Preprocessing/13. Variable transformation and deletion in Python.srt 9.3 KB
1. Setting up Python and Jupyter notebook/10. Working with Seaborn Library of Python.srt 9.1 KB
5. Linear Regression/12. Test train split in Python.srt 8.8 KB
4. Data Preprocessing/3. The Dataset and the Data Dictionary.srt 8.5 KB
5. Linear Regression/11. Bias Variance trade-off.srt 8.2 KB
2. Basics of statistics/4. Measures of Centers.srt 8.1 KB
5. Linear Regression/6. Multiple Linear Regression.srt 7.4 KB
6. Introduction to the classification Models/1. Three classification models and Data set.srt 7.2 KB
4. Data Preprocessing/18. Correlation Analysis in Python.srt 7.2 KB
5. Linear Regression/8. Interpreting results of Categorical variables.srt 6.9 KB
4. Data Preprocessing/4. Importing Data in Python.srt 6.6 KB
4. Data Preprocessing/14. Non-usable variables.srt 6.5 KB
4. Data Preprocessing/16. Dummy variable creation in Python.srt 6.4 KB
6. Introduction to the classification Models/4. Why can't we use Linear Regression.srt 5.7 KB
4. Data Preprocessing/15. Dummy variable creation Handling qualitative data.srt 5.5 KB
5. Linear Regression/13. Regression models other than OLS.srt 5.3 KB
2. Basics of statistics/5. Measures of Dispersion.srt 5.3 KB
2. Basics of statistics/1. Types of Data.srt 5.2 KB
4. Data Preprocessing/7. Outlier Treatment.srt 5.0 KB
4. Data Preprocessing/10. Missing Value Imputation in Python.srt 4.7 KB
1. Setting up Python and Jupyter notebook/5. Arithmetic operators in Python Python Basics.srt 4.6 KB
4. Data Preprocessing/9. Missing Value Imputation.srt 4.2 KB
4. Data Preprocessing/11. Seasonality in Data.srt 4.1 KB
1. Setting up Python and Jupyter notebook/2. This is a Milestone!.srt 3.9 KB
4. Data Preprocessing/2. Data Exploration.srt 3.8 KB
4. Data Preprocessing/1. Gathering Business Knowledge.srt 3.8 KB
4. Data Preprocessing/5. Univariate analysis and EDD.srt 3.8 KB
2. Basics of statistics/2. Types of Statistics.srt 3.3 KB
5. Linear Regression/17. Heteroscedasticity.srt 3.2 KB
1. Setting up Python and Jupyter notebook/1. Installing Python and Anaconda.srt 2.7 KB
26. Congratulations & about your certificate/1. Bonus Lecture.html 2.3 KB
6. Introduction to the classification Models/3. The problem statements.srt 1.9 KB
5. Linear Regression/1. The Problem Statement.srt 1.8 KB
6. Introduction to the classification Models/2. Importing the data into Python.srt 1.7 KB
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