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This course teaches you how to code basic machine learning models.

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Here is what you will Learn from A Practical Guide to Machine Learning with Python

1. 1. Introduction to Course
2. Getting Started
3. Why Learn Machine Learning
4. 2. Introduction to Machine Learning
5. Types of Self Learning
6. Introduction to Datasets
7. Machine Learning Libraries
8. 3. Exploratory Data Analysis
9. Import Dataset
10. Data Frame Functions
11. 4. Data Scrubbing
12. Introduction to Data Scrubbing
13. Data Scrubbing Operation: Removing Variables
14. Data Scrubbing Operation: One-Hot Encoding
15. Data Scrubbing Operation: Drop Missing Values
16. Data Scrubbing Operation: Dimension Reduction
17. 5. Pre-Model Algorithms
18. PCA Implementation Steps: 1 to 3
19. PCA Implementation Steps: 4 to 6
20. K-Means Clustering Implementation Steps: 1 to 3
21. K-Means Clustering Implementation Steps: 4 to 6
22. 6. Split Validation
23. Quick Overview of Split Validation
24. 7. Model Design
25. Introduction to Model Design
26. MD Implementation Steps: 3 to 8
27. MD Implementation Steps: 9 and 10
28. 8. Linear Regression
29. Implementation of Linear Regression
30. LR Implementation Steps: 1 to 3
31. LR Implementation Steps: 4 to 7
32. LR Implementation Steps: 8 and 9
33. 9. Logistic Regression
34. Implementation of Logistic Regression
35. Logistic Regression Steps: 1 to 4
36. Logistic Regression Steps: 5 to 7
37. Logistic Regression Steps: 8 and 9
38. 10. Support Vector Machines
39. Implementation of Support Vector Machines
40. SVM Implementation Steps: 1 to 7
41. SVM Implementation Steps: 8 and 9
42. 11. K-Nearest Neighbors
43. Introduction to K-Nearest Neighbors
44. k-NN Implementation Steps: 1 to 5
45. k-NN Implementation Steps: 6 to 9
46. 12. Tree-Based Methods
47. Introduction to Tree-Based Methods
48. 1- Decision Trees
49. DT Implementation Steps: 1 to 4
50. DT Implementation Steps: 5 to 7
51. 2- Random Forests
52. Implementation of Random Forest
53. 3- Gradient Boosting
54. Implementation of Gradient Boosting Classifier
55. Implementation of Gradient Boosting Regressor
56. 13. Conclusion
57. What’s Next?
58. 14. Appendix
59. Basics of Python Programming
60. Development Environment