[FreeCourseSite.com] Udemy - Machine Learning using Python

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[FreeCourseSite.com] Udemy - Machine Learning using Python

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种子哈希：55995f6674d15a1613c0a25105fe8e1aa1989b04
文件大小： 7.06G
已经下载：3533次
下载速度：极快
收录时间：2022-10-06
最近下载：2025-10-10

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文件列表

  • 21. Time Series - Preprocessing in Pyhton/3. Time Series - Visualization in Python.mp4 218.4 MB
  • 5. Linear Regression/16. Ridge regression and Lasso in Python.mp4 183.4 MB
  • 21. Time Series - Preprocessing in Pyhton/5. Time Series - Feature Engineering in Python.mp4 149.4 MB
  • 22. Time Series - Important Concepts/5. Differencing in Python.mp4 148.0 MB
  • 21. Time Series - Preprocessing in Pyhton/1. Data Loading in Python.mp4 141.1 MB
  • 21. Time Series - Preprocessing in Pyhton/7. Time Series - Upsampling and Downsampling in Python.mp4 130.3 MB
  • 3. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 118.8 MB
  • 5. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 108.2 MB
  • 4. Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4 105.3 MB
  • 12. Simple Classification Tree/4. Classification tree in Python Training.mp4 104.4 MB
  • 4. Data Preprocessing/8. Outlier Treatment in Python.mp4 103.1 MB
  • 13. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4 101.8 MB
  • 14. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4 96.2 MB
  • 5. Linear Regression/9. Multiple Linear Regression in Python.mp4 89.3 MB
  • 24. Time Series - ARIMA model/3. ARIMA model in Python.mp4 89.0 MB
  • 5. Linear Regression/5. Simple Linear Regression in Python.mp4 89.0 MB
  • 21. Time Series - Preprocessing in Pyhton/2. Time Series - Visualization Basics.mp4 84.2 MB
  • 5. Linear Regression/14. Subset selection techniques.mp4 82.9 MB
  • 20. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4 82.7 MB
  • 22. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4 82.4 MB
  • 4. Data Preprocessing/6. EDD in Python.mp4 82.3 MB
  • 23. Time Series - Implementation in Python/1. Test Train Split in Python.mp4 80.9 MB
  • 21. Time Series - Preprocessing in Pyhton/4. Time Series - Feature Engineering Basics.mp4 80.7 MB
  • 4. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 80.0 MB
  • 2. Basics of statistics/3. Describing data Graphically.mp4 79.7 MB
  • 9. K Nearest neighbors classifier/3. K-Nearest Neighbors classifier.mp4 79.0 MB
  • 25. Time Series - SARIMA model/2. SARIMA model in Python.mp4 78.8 MB
  • 15. Ensemble technique 3 - Boosting/4. Ensemble technique 3c - XGBoost in Python.mp4 78.6 MB
  • 4. Data Preprocessing/17. Correlation Analysis.mp4 78.3 MB
  • 19. Creating Support Vector Machine Model in Python/4. SVM based Regression Model in Python.mp4 77.3 MB
  • 17. Support Vector classifiers/1. Support Vector classifiers.mp4 77.3 MB
  • 19. Creating Support Vector Machine Model in Python/7. SVM Based classification model.mp4 76.4 MB
  • 7. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4 72.9 MB
  • 1. Setting up Python and Jupyter notebook/3. Opening Jupyter Notebook.mp4 71.8 MB
  • 1. Setting up Python and Jupyter notebook/6. Strings in Python Python Basics.mp4 71.5 MB
  • 23. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4 70.9 MB
  • 19. Creating Support Vector Machine Model in Python/8. Hyper Parameter Tuning.mp4 70.8 MB
  • 4. Data Preprocessing/13. Variable transformation and deletion in Python.mp4 70.6 MB
  • 4. Data Preprocessing/18. Correlation Analysis in Python.mp4 68.8 MB
  • 23. Time Series - Implementation in Python/7. Moving Average model in Python.mp4 67.4 MB
  • 5. Linear Regression/12. Test train split in Python.mp4 67.1 MB
  • 1. Setting up Python and Jupyter notebook/7. Lists, Tuples and Directories Python Basics.mp4 66.3 MB
  • 23. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4 64.8 MB
  • 11. Simple Decision Trees/3. Understanding a Regression Tree.mp4 64.1 MB
  • 7. Logistic Regression/7. Creating Confusion Matrix in Python.mp4 63.7 MB
  • 9. K Nearest neighbors classifier/2. Test-Train Split in Python.mp4 61.9 MB
  • 11. Simple Decision Trees/2. Basics of Decision Trees.mp4 61.5 MB
  • 23. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4 59.6 MB
  • 14. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4 57.5 MB
  • 19. Creating Support Vector Machine Model in Python/5. Classification model - Preprocessing.mp4 56.5 MB
  • 12. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4 56.4 MB
  • 5. Linear Regression/7. The F - statistic.mp4 56.4 MB
  • 18. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4 55.9 MB
  • 9. K Nearest neighbors classifier/5. K-Nearest Neighbors in Python Part 2.mp4 55.7 MB
  • 24. Time Series - ARIMA model/1. ACF and PACF.mp4 55.3 MB
  • 6. Introduction to the classification Models/1. Three classification models and Data set.mp4 54.8 MB
  • 1. Setting up Python and Jupyter notebook/9. Working with Pandas Library of Python.mp4 53.2 MB
  • 21. Time Series - Preprocessing in Pyhton/9. Moving Average.mp4 52.5 MB
  • 1. Setting up Python and Jupyter notebook/8. Working with Numpy Library of Python.mp4 48.7 MB
  • 9. K Nearest neighbors classifier/4. K-Nearest Neighbors in Python Part 1.mp4 48.4 MB
  • 20. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp4 48.2 MB
  • 5. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 47.2 MB
  • 11. Simple Decision Trees/1. Introduction to Decision trees.mp4 47.1 MB
  • 19. Creating Support Vector Machine Model in Python/10. Radial Kernel with Hyperparameter Tuning.mp4 46.7 MB
  • 1. Setting up Python and Jupyter notebook/4. Introduction to Jupyter.mp4 46.2 MB
  • 22. Time Series - Important Concepts/4. Differencing.mp4 46.1 MB
  • 2. Basics of statistics/4. Measures of Centers.mp4 45.4 MB
  • 5. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 44.6 MB
  • 19. Creating Support Vector Machine Model in Python/3. Standardizing the data.mp4 44.1 MB
  • 5. Linear Regression/10. Test-train split.mp4 43.9 MB
  • 10. Comparing results from 3 models/1. Understanding the results of classification models.mp4 43.7 MB
  • 3. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4 43.0 MB
  • 8. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4 42.9 MB
  • 15. Ensemble technique 3 - Boosting/1. Boosting.mp4 42.9 MB
  • 4. Data Preprocessing/16. Dummy variable creation in Python.mp4 42.8 MB
  • 16. Support Vector Machines/2. The Concept of a Hyperplane.mp4 42.5 MB
  • 4. Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp4 42.4 MB
  • 12. Simple Classification Tree/1. Classification tree.mp4 42.2 MB
  • 25. Time Series - SARIMA model/1. SARIMA model.mp4 42.2 MB
  • 15. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4 41.8 MB
  • 1. Setting up Python and Jupyter notebook/10. Working with Seaborn Library of Python.mp4 41.5 MB
  • 13. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4 41.2 MB
  • 9. K Nearest neighbors classifier/1. Test-Train Split.mp4 41.2 MB
  • 5. Linear Regression/6. Multiple Linear Regression.mp4 40.0 MB
  • 24. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4 37.9 MB
  • 7. Logistic Regression/8. Evaluating performance of model.mp4 36.9 MB
  • 7. Logistic Regression/5. Training multiple predictor Logistic model in Python.mp4 35.9 MB
  • 4. Data Preprocessing/4. Importing Data in Python.mp4 35.6 MB
  • 4. Data Preprocessing/10. Missing Value Imputation in Python.mp4 34.9 MB
  • 5. Linear Regression/15. Shrinkage methods Ridge and Lasso.mp4 34.9 MB
  • 7. Logistic Regression/1. Logistic Regression.mp4 34.5 MB
  • 23. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4 33.3 MB
  • 20. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4 32.9 MB
  • 16. Support Vector Machines/3. Maximum Margin Classifier.mp4 32.1 MB
  • 15. Ensemble technique 3 - Boosting/3. Ensemble technique 3b - AdaBoost in Python.mp4 32.0 MB
  • 4. Data Preprocessing/5. Univariate analysis and EDD.mp4 30.7 MB
  • 4. Data Preprocessing/2. Data Exploration.mp4 29.8 MB
  • 22. Time Series - Important Concepts/2. Random Walk.mp4 29.4 MB
  • 11. Simple Decision Trees/12. Plotting decision tree in Python.mp4 28.4 MB
  • 7. Logistic Regression/3. Result of Simple Logistic Regression.mp4 28.2 MB
  • 4. Data Preprocessing/7. Outlier Treatment.mp4 27.9 MB
  • 24. Time Series - ARIMA model/2. ARIMA model - Basics.mp4 27.8 MB
  • 19. Creating Support Vector Machine Model in Python/2. Importing and preprocessing data in Python.mp4 27.7 MB
  • 19. Creating Support Vector Machine Model in Python/9. Polynomial Kernel with Hyperparameter Tuning.mp4 27.7 MB
  • 2. Basics of statistics/5. Measures of Dispersion.mp4 27.6 MB
  • 14. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4 27.3 MB
  • 11. Simple Decision Trees/9. Test-Train split in Python.mp4 26.9 MB
  • 5. Linear Regression/11. Bias Variance trade-off.mp4 26.3 MB
  • 11. Simple Decision Trees/14. Pruning a tree in Python.mp4 26.3 MB
  • 11. Simple Decision Trees/13. Pruning a tree.mp4 26.3 MB
  • 11. Simple Decision Trees/7. Dummy Variable Creation in Python.mp4 25.8 MB
  • 4. Data Preprocessing/9. Missing Value Imputation.mp4 25.7 MB
  • 21. Time Series - Preprocessing in Pyhton/6. Time Series - Upsampling and Downsampling.mp4 24.5 MB
  • 2. Basics of statistics/1. Types of Data.mp4 24.4 MB
  • 10. Comparing results from 3 models/2. Summary of the three models.mp4 23.3 MB
  • 16. Support Vector Machines/1. Introduction to SVM's.mp4 22.7 MB
  • 5. Linear Regression/8. Interpreting results of Categorical variables.mp4 22.5 MB
  • 11. Simple Decision Trees/10. Creating Decision tree in Python.mp4 22.4 MB
  • 7. Logistic Regression/6. Confusion Matrix.mp4 22.1 MB
  • 23. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4 21.9 MB
  • 12. Simple Classification Tree/2. The Data set for Classification problem.mp4 21.9 MB
  • 1. Setting up Python and Jupyter notebook/2. This is a Milestone!.mp4 21.7 MB
  • 4. Data Preprocessing/14. Non-usable variables.mp4 21.2 MB
  • 11. Simple Decision Trees/4. The stopping criteria for controlling tree growth.mp4 20.3 MB
  • 21. Time Series - Preprocessing in Pyhton/8. Time Series - Power Transformation.mp4 19.6 MB
  • 20. Time Series Analysis and Forecasting/1. Introduction.mp4 19.6 MB
  • 11. Simple Decision Trees/11. Evaluating model performance in Python.mp4 19.2 MB
  • 1. Setting up Python and Jupyter notebook/1. Installing Python and Anaconda.mp4 18.9 MB
  • 8. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4 18.5 MB
  • 4. Data Preprocessing/1. Gathering Business Knowledge.mp4 18.1 MB
  • 6. Introduction to the classification Models/3. The problem statements.mp4 17.9 MB
  • 4. Data Preprocessing/11. Seasonality in Data.mp4 17.8 MB
  • 6. Introduction to the classification Models/4. Why can't we use Linear Regression.mp4 17.7 MB
  • 11. Simple Decision Trees/8. Dependent- Independent Data split in Python.mp4 17.7 MB
  • 5. Linear Regression/13. Regression models other than OLS.mp4 17.3 MB
  • 11. Simple Decision Trees/5. Importing the Data set into Python.mp4 16.6 MB
  • 17. Support Vector classifiers/2. Limitations of Support Vector Classifiers.mp4 16.4 MB
  • 22. Time Series - Important Concepts/1. White Noise.mp4 15.4 MB
  • 16. Support Vector Machines/4. Limitations of Maximum Margin Classifier.mp4 15.2 MB
  • 5. Linear Regression/17. Heteroscedasticity.mp4 15.2 MB
  • 1. Setting up Python and Jupyter notebook/5. Arithmetic operators in Python Python Basics.mp4 14.2 MB
  • 7. Logistic Regression/9. Evaluating model performance in Python.mp4 14.0 MB
  • 11. Simple Decision Trees/6. Missing value treatment in Python.mp4 13.6 MB
  • 20. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4 12.7 MB
  • 2. Basics of statistics/2. Types of Statistics.mp4 12.6 MB
  • 25. Time Series - SARIMA model/4. The final milestone!.mp4 12.4 MB
  • 21. Time Series - Preprocessing in Pyhton/10. Exponential Smoothing.mp4 11.4 MB
  • 19. Creating Support Vector Machine Model in Python/6. Classification model - Standardizing the data.mp4 11.1 MB
  • 5. Linear Regression/1. The Problem Statement.mp4 10.6 MB
  • 12. Simple Classification Tree/5. Advantages and Disadvantages of Decision Trees.mp4 10.5 MB
  • 7. Logistic Regression/4. Logistic with multiple predictors.mp4 8.9 MB
  • 6. Introduction to the classification Models/2. Importing the data into Python.mp4 7.2 MB
  • 25. Time Series - SARIMA model/3. Stationary time Series.mp4 5.9 MB
  • 19. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4 4.9 MB
  • 1. Setting up Python and Jupyter notebook/7. Lists, Tuples and Directories Python Basics.srt 22.7 kB
  • 5. Linear Regression/16. Ridge regression and Lasso in Python.srt 22.0 kB
  • 4. Data Preprocessing/12. Bi-variate analysis and Variable transformation.srt 20.7 kB
  • 5. Linear Regression/3. Assessing accuracy of predicted coefficients.srt 20.4 kB
  • 3. Introduction to Machine Learning/1. Introduction to Machine Learning.srt 19.8 kB
  • 1. Setting up Python and Jupyter notebook/6. Strings in Python Python Basics.srt 19.0 kB
  • 1. Setting up Python and Jupyter notebook/4. Introduction to Jupyter.srt 15.9 kB
  • 5. Linear Regression/14. Subset selection techniques.srt 15.6 kB
  • 4. Data Preprocessing/8. Outlier Treatment in Python.srt 14.8 kB
  • 5. Linear Regression/9. Multiple Linear Regression in Python.srt 14.8 kB
  • 5. Linear Regression/5. Simple Linear Regression in Python.srt 13.7 kB
  • 2. Basics of statistics/3. Describing data Graphically.srt 13.5 kB
  • 1. Setting up Python and Jupyter notebook/8. Working with Numpy Library of Python.srt 13.1 kB
  • 5. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 13.0 kB
  • 5. Linear Regression/10. Test-train split.srt 12.9 kB
  • 4. Data Preprocessing/6. EDD in Python.srt 12.1 kB
  • 4. Data Preprocessing/17. Correlation Analysis.srt 12.1 kB
  • 5. Linear Regression/7. The F - statistic.srt 11.7 kB
  • 1. Setting up Python and Jupyter notebook/9. Working with Pandas Library of Python.srt 10.6 kB
  • 3. Introduction to Machine Learning/2. Building a Machine Learning Model.srt 10.5 kB
  • 1. Setting up Python and Jupyter notebook/3. Opening Jupyter Notebook.srt 10.3 kB
  • 5. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 10.0 kB
  • 5. Linear Regression/15. Shrinkage methods Ridge and Lasso.srt 9.6 kB
  • 4. Data Preprocessing/13. Variable transformation and deletion in Python.srt 9.5 kB
  • 1. Setting up Python and Jupyter notebook/10. Working with Seaborn Library of Python.srt 9.3 kB
  • 5. Linear Regression/12. Test train split in Python.srt 9.0 kB
  • 4. Data Preprocessing/3. The Dataset and the Data Dictionary.srt 8.7 kB
  • 5. Linear Regression/11. Bias Variance trade-off.srt 8.4 kB
  • 2. Basics of statistics/4. Measures of Centers.srt 8.3 kB
  • 5. Linear Regression/6. Multiple Linear Regression.srt 7.6 kB
  • 6. Introduction to the classification Models/1. Three classification models and Data set.srt 7.4 kB
  • 4. Data Preprocessing/18. Correlation Analysis in Python.srt 7.4 kB
  • 5. Linear Regression/8. Interpreting results of Categorical variables.srt 7.1 kB
  • 4. Data Preprocessing/4. Importing Data in Python.srt 6.8 kB
  • 4. Data Preprocessing/14. Non-usable variables.srt 6.7 kB
  • 4. Data Preprocessing/16. Dummy variable creation in Python.srt 6.6 kB
  • 6. Introduction to the classification Models/4. Why can't we use Linear Regression.srt 5.8 kB
  • 4. Data Preprocessing/15. Dummy variable creation Handling qualitative data.srt 5.7 kB
  • 5. Linear Regression/13. Regression models other than OLS.srt 5.4 kB
  • 2. Basics of statistics/5. Measures of Dispersion.srt 5.4 kB
  • 2. Basics of statistics/1. Types of Data.srt 5.3 kB
  • 4. Data Preprocessing/7. Outlier Treatment.srt 5.1 kB
  • 4. Data Preprocessing/10. Missing Value Imputation in Python.srt 4.8 kB
  • 1. Setting up Python and Jupyter notebook/5. Arithmetic operators in Python Python Basics.srt 4.7 kB
  • 4. Data Preprocessing/9. Missing Value Imputation.srt 4.3 kB
  • 4. Data Preprocessing/11. Seasonality in Data.srt 4.2 kB
  • 1. Setting up Python and Jupyter notebook/2. This is a Milestone!.srt 4.0 kB
  • 4. Data Preprocessing/2. Data Exploration.srt 3.9 kB
  • 4. Data Preprocessing/1. Gathering Business Knowledge.srt 3.9 kB
  • 4. Data Preprocessing/5. Univariate analysis and EDD.srt 3.8 kB
  • 2. Basics of statistics/2. Types of Statistics.srt 3.4 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 2.4 kB
  • 6. Introduction to the classification Models/3. The problem statements.srt 2.0 kB
  • 5. Linear Regression/1. The Problem Statement.srt 1.9 kB
  • 6. Introduction to the classification Models/2. Importing the data into Python.srt 1.8 kB
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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