DesireCourse Net Udemy - Machine Learning Basics Classification models in Python

1. 1. Introduction/1. Welcome to the course!.mp417.6MB
2. 2. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4123.83MB
3. 2. Introduction to Machine Learning/2. Building a Machine Learning model.mp445.27MB
4. 3. Basics of Statistics/1. Types of Data.mp425.86MB
5. 3. Basics of Statistics/2. Types of Statistics.mp413.24MB
6. 3. Basics of Statistics/3. Describing data Graphically.mp482.16MB
7. 3. Basics of Statistics/4. Measures of Centers.mp445.69MB
8. 3. Basics of Statistics/6. Measures of Dispersion.mp428.38MB
9. 4. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp418.64MB
10. 4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp473.06MB
11. 4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp451.28MB
12. 4. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp415.97MB
13. 4. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp480.59MB
14. 4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp473.66MB
15. 4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp454.1MB
16. 4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp456.4MB
17. 4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp448.86MB
18. 5. Data Preprocessing/1. Gathering Business Knowledge.mp425.13MB
19. 5. Data Preprocessing/10. Outlier treatment in Python.mp458.44MB
20. 5. Data Preprocessing/12. Missing Value Imputation.mp427.56MB
21. 5. Data Preprocessing/13. Missing Value Imputation in Python.mp427.65MB
22. 5. Data Preprocessing/15. Seasonality in Data.mp420.88MB
23. 5. Data Preprocessing/16. Variable Transformation.mp415.29MB
24. 5. Data Preprocessing/17. Variable transformation and Deletion in Python.mp435.58MB
25. 5. Data Preprocessing/19. Dummy variable creation Handling qualitative data.mp440.6MB
26. 5. Data Preprocessing/2. Data Exploration.mp423.4MB
27. 5. Data Preprocessing/20. Dummy variable creation in Python.mp433.87MB
28. 5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp487.62MB
29. 5. Data Preprocessing/4. Data Import in Python.mp425.49MB
30. 5. Data Preprocessing/6. Univariate analysis and EDD.mp427.31MB
31. 5. Data Preprocessing/7. EDD in Python.mp497.09MB
32. 5. Data Preprocessing/9. Outlier Treatment.mp427.77MB
33. 6. Classification Models/1. Three Classifiers and the problem statement.mp422.93MB
34. 6. Classification Models/10. Confusion Matrix.mp426.65MB
35. 6. Classification Models/11. Making Confusion Matrix in Python.mp464.71MB
36. 6. Classification Models/12. Evaluating performance of model.mp442.79MB
37. 6. Classification Models/13. Evaluating model performance in Python.mp411.77MB
38. 6. Classification Models/15. Linear Discriminant Analysis.mp448.68MB
39. 6. Classification Models/16. LDA in Python.mp414.38MB
40. 6. Classification Models/18. Test-Train Split.mp445.69MB
41. 6. Classification Models/19. Test-Train Split in Python.mp443.1MB
42. 6. Classification Models/2. Why can't we use Linear Regression.mp420.4MB
43. 6. Classification Models/21. K-Nearest Neighbors classifier.mp483.57MB
44. 6. Classification Models/22. K-Nearest Neighbors in Python Part 1.mp445.85MB
45. 6. Classification Models/23. K-Nearest Neighbors in Python Part 2.mp451.98MB
46. 6. Classification Models/25. Understanding the results of classification models.mp445.98MB
47. 6. Classification Models/26. Summary of the three models.mp425.25MB
48. 6. Classification Models/3. Logistic Regression.mp439.11MB
49. 6. Classification Models/4. Training a Simple Logistic Model in Python.mp461.19MB
50. 6. Classification Models/6. Result of Simple Logistic Regression.mp431.15MB
51. 6. Classification Models/7. Logistic with multiple predictors.mp49.99MB
52. 6. Classification Models/8. Training multiple predictor Logistic model in Python.mp434.02MB