DesireCourse Com Udemy - Machine Learning Basics Building Regression Model in Python

1. 1. Introduction/1. Welcome to the course!.mp416.27MB
2. 1. Introduction/2. Course contents.mp447.85MB
3. 2. Basics of Statistics/1. Types of Data.mp425.86MB
4. 2. Basics of Statistics/2. Types of Statistics.mp413.23MB
5. 2. Basics of Statistics/3. Describing data Graphically.mp482.21MB
6. 2. Basics of Statistics/4. Measures of Centers.mp445.67MB
7. 2. Basics of Statistics/6. Measures of Dispersion.mp428.39MB
8. 3. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp418.61MB
9. 3. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp473.07MB
10. 3. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp451.3MB
11. 3. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp415.92MB
12. 3. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp480.64MB
13. 3. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp473.67MB
14. 3. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp454.13MB
15. 3. Setting up Python and Jupyter Notebook/8. Working with Panda Library of Python.mp456.46MB
16. 3. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp448.87MB
17. 4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4123.9MB
18. 4. Introduction to Machine Learning/2. Building a Machine Learning Model.mp445.26MB
19. 5. Data Preprocessing/1. Gathering Business Knowledge.mp425.11MB
20. 5. Data Preprocessing/10. Outlier Treatment in Python.mp486.57MB
21. 5. Data Preprocessing/12. Missing Value Imputation.mp427.56MB
22. 5. Data Preprocessing/13. Missing Value Imputation in Python.mp428.59MB
23. 5. Data Preprocessing/15. Seasonality in Data.mp420.88MB
24. 5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4113.73MB
25. 5. Data Preprocessing/17. Variable transformation and deletion in Python.mp453.4MB
26. 5. Data Preprocessing/19. Non-usable variables.mp423.95MB
27. 5. Data Preprocessing/2. Data Exploration.mp423.41MB
28. 5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp440.63MB
29. 5. Data Preprocessing/21. Dummy variable creation in Python.mp433.89MB
30. 5. Data Preprocessing/23. Correlation Analysis.mp481.3MB
31. 5. Data Preprocessing/24. Correlation Analysis in Python.mp468.03MB
32. 5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp478.59MB
33. 5. Data Preprocessing/4. Importing Data in Python.mp432.46MB
34. 5. Data Preprocessing/6. Univariate analysis and EDD.mp427.3MB
35. 5. Data Preprocessing/7. EDD in Python.mp475.08MB
36. 5. Data Preprocessing/9. Outlier Treatment.mp427.78MB
37. 6. Linear Regression/1. The Problem Statement.mp410.68MB
38. 6. Linear Regression/10. Multiple Linear Regression in Python.mp488.14MB
39. 6. Linear Regression/12. Test-train split.mp449.14MB
40. 6. Linear Regression/13. Bias Variance trade-off.mp429.6MB
41. 6. Linear Regression/14. Test train split in Python.mp457.77MB
42. 6. Linear Regression/15. Linear models other than OLS.mp419.18MB
43. 6. Linear Regression/16. Subset selection techniques.mp487.14MB
44. 6. Linear Regression/17. Shrinkage methods Ridge and Lasso.mp438.64MB
45. 6. Linear Regression/18. Ridge regression and Lasso in Python.mp4156.64MB
46. 6. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp450.27MB
47. 6. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4104.42MB
48. 6. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp449.72MB
49. 6. Linear Regression/5. Simple Linear Regression in Python.mp478.63MB
50. 6. Linear Regression/7. Multiple Linear Regression.mp438.91MB
51. 6. Linear Regression/8. The F - statistic.mp464.12MB
52. 6. Linear Regression/9. Interpreting results of Categorical variables.mp427.13MB