[DesireCourse Com] Udemy Machine Learning Basics Building Regression Model in Python Torrent Download Locations

[DesireCourse.Com] Udemy - Machine Learning Basics Building Regression Model in Python
    1. Introduction
          1. Welcome to the course!.mp4 -

          1. Welcome to the course!.vtt -

          2. Course contents.mp4 -

          2. Course contents.vtt -

          2.1 00_Introduction_01.pdf.pdf -

    2. Basics of Statistics
          1. Types of Data.mp4 -

          1. Types of Data.vtt -

          1.1 01_01_Lecture_TypesOfData.pdf.pdf -

          2. Types of Statistics.mp4 -

          2. Types of Statistics.vtt -

          2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf -

          3. Describing data Graphically.mp4 -

          3. Describing data Graphically.vtt -

          3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf -

          4. Measures of Centers.mp4 -

          4. Measures of Centers.vtt -

          4.1 01_04_Lecture_Centers.pdf.pdf -

          5. Practice Exercise 1.html -

          5.1 Exercise 1.pdf.pdf -

          6. Measures of Dispersion.mp4 -

          6. Measures of Dispersion.vtt -

          6.1 01_05_Lecture_Dispersion.pdf.pdf -

          7. Practice Exercise 2.html -

          7.1 Exercise 2.pdf.pdf -

    3. Setting up Python and Jupyter Notebook
          1. Installing Python and Anaconda.mp4 -

          1. Installing Python and Anaconda.vtt -

          2. Opening Jupyter Notebook.mp4 -

          2. Opening Jupyter Notebook.vtt -

          3. Introduction to Jupyter.mp4 -

          3. Introduction to Jupyter.vtt -

          4. Arithmetic operators in Python Python Basics.mp4 -

          4. Arithmetic operators in Python Python Basics.vtt -

          5. Strings in Python Python Basics.mp4 -

          5. Strings in Python Python Basics.vtt -

          6. Lists, Tuples and Directories Python Basics.mp4 -

          6. Lists, Tuples and Directories Python Basics.vtt -

          7. Working with Numpy Library of Python.mp4 -

          7. Working with Numpy Library of Python.vtt -

          8. Working with Panda Library of Python.mp4 -

          8. Working with Panda Library of Python.vtt -

          8.1 Customer.csv.csv -

          9. Working with Seaborn Library of Python.mp4 -

          9. Working with Seaborn Library of Python.vtt -

    4. Introduction to Machine Learning
          1. Introduction to Machine Learning.mp4 -

          1. Introduction to Machine Learning.vtt -

          1.1 Lecture_machineLearning.pdf.pdf -

          2. Building a Machine Learning Model.mp4 -

          2. Building a Machine Learning Model.vtt -

          2.1 Lecture_machineLearning.pdf.pdf -

          3. Introduction to Machine learning quiz.html -

    5. Data Preprocessing
          1. Gathering Business Knowledge.mp4 -

          1. Gathering Business Knowledge.vtt -

          1.1 03_01_PDE_Business_knowledge.pdf.pdf -

          10. Outlier Treatment in Python.mp4 -

          10. Outlier Treatment in Python.vtt -

          11. Project Exercise 3.html -

          12. Missing Value Imputation.mp4 -

          12. Missing Value Imputation.vtt -

          12.1 04_05_PDE_Missing_value.pdf.pdf -

          13. Missing Value Imputation in Python.mp4 -

          13. Missing Value Imputation in Python.vtt -

          14. Project Exercise 4.html -

          15. Seasonality in Data.mp4 -

          15. Seasonality in Data.vtt -

          15.1 04_07_PDE_Seasonality.pdf.pdf -

          16. Bi-variate analysis and Variable transformation.mp4 -

          16. Bi-variate analysis and Variable transformation.vtt -

          16.1 04_07_Variable_Transformation.pdf.pdf -

          17. Variable transformation and deletion in Python.mp4 -

          17. Variable transformation and deletion in Python.vtt -

          18. Project Exercise 5.html -

          19. Non-usable variables.mp4 -

          19. Non-usable variables.vtt -

          19.1 04_08_PDE_Non_Usable_var.pdf.pdf -

          2. Data Exploration.mp4 -

          2. Data Exploration.vtt -

          2.1 03_02_PDE_Data_exploration.pdf.pdf -

          20. Dummy variable creation Handling qualitative data.mp4 -

          20. Dummy variable creation Handling qualitative data.vtt -

          20.1 04_11_Dummy_Var.pdf.pdf -

          21. Dummy variable creation in Python.mp4 -

          21. Dummy variable creation in Python.vtt -

          22. Project Exercise 6.html -

          23. Correlation Analysis.mp4 -

          23. Correlation Analysis.vtt -

          23.1 04_10_Correlation.pdf.pdf -

          24. Correlation Analysis in Python.mp4 -

          24. Correlation Analysis in Python.vtt -

          25. Project Exercise 7.html -

          3. The Dataset and the Data Dictionary.mp4 -

          3. The Dataset and the Data Dictionary.vtt -

          3.1 03_03_PDE_Raw_Data_Analysis_Uni.pdf.pdf -

          3.2 House_Price.csv.csv -

          4. Importing Data in Python.mp4 -

          4. Importing Data in Python.vtt -

          4.1 House_Price.csv.csv -

          5. Project exercise 1.html -

          5.1 Movie_collection_train.csv.csv -

          6. Univariate analysis and EDD.mp4 -

          6. Univariate analysis and EDD.vtt -

          6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf -

          7. EDD in Python.mp4 -

          7. EDD in Python.vtt -

          8. Project Exercise 2.html -

          9. Outlier Treatment.mp4 -

          9. Outlier Treatment.vtt -

          9.1 04_06_PDE_Outlier_Treatment.pdf.pdf -

    6. Linear Regression
          1. The Problem Statement.mp4 -

          1. The Problem Statement.vtt -

          1.1 05_01_Intro.pdf.pdf -

          10. Multiple Linear Regression in Python.mp4 -

          10. Multiple Linear Regression in Python.vtt -

          11. Project Exercise 9.html -

          12. Test-train split.mp4 -

          12. Test-train split.vtt -

          12.1 05_12_Test_Train.pdf.pdf -

          13. Bias Variance trade-off.mp4 -

          13. Bias Variance trade-off.vtt -

          13.1 05_13_Bias_Var_tradeoff.pdf.pdf -

          14. Test train split in Python.mp4 -

          14. Test train split in Python.vtt -

          15. Linear models other than OLS.mp4 -

          15. Linear models other than OLS.vtt -

          15.1 05_09_Other_lin_model.pdf.pdf -

          16. Subset selection techniques.mp4 -

          16. Subset selection techniques.vtt -

          16.1 05_10_Subset_Selection.pdf.pdf -

          17. Shrinkage methods Ridge and Lasso.mp4 -

          17. Shrinkage methods Ridge and Lasso.vtt -

          17.1 05_11_Shrinkage_methods.pdf.pdf -

          18. Ridge regression and Lasso in Python.mp4 -

          18. Ridge regression and Lasso in Python.vtt -

          19. Project Exercise 10.html -

          2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 -

          2. Basic Equations and Ordinary Least Squares (OLS) method.vtt -

          2.1 05_02_Simple_lin_reg.pdf.pdf -

          20. Final Project Exercise.html -

          20.1 Movie_collection_test.csv.csv -

          21. Course Conclusion.html -

          3. Assessing accuracy of predicted coefficients.mp4 -

          3. Assessing accuracy of predicted coefficients.vtt -

          3.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf -

          4. Assessing Model Accuracy RSE and R squared.mp4 -

          4. Assessing Model Accuracy RSE and R squared.vtt -

          4.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf -

          5. Simple Linear Regression in Python.mp4 -

          5. Simple Linear Regression in Python.vtt -

          6. Project Exercise 8.html -

          7. Multiple Linear Regression.mp4 -

          7. Multiple Linear Regression.vtt -