Self-Paced Online

Machine Learning Classification Bootcamp

Machine Learning Classification Bootcamp: a self-paced PACI course with 88 lessons in 11 sections and a quiz after each section.

Machine Learning
English language
Beginner

What you’ll learn

What is Machine Learning The Big Picture
Installation & Setup[Optional] [Skip If You arae Familiar With Jupter Notebooks
Logistic Regression
Logistic Regression Part 02
Support Vector Machines
K-Nearest Neighbors

About this course

Machine Learning Classification Bootcamp is a self-paced recorded course from the Punjab Artificial Intelligence and Cybersecurity Initiative (PACI). It has 88 lessons in 11 sections (78 video lessons, 9 quizzes, 1 reading / resource lessons). What the course covers: - What is Machine Learning The Big Picture - Installation & Setup[Optional] [Skip If You arae Familiar With Jupter Notebooks - Logistic Regression - Logistic Regression Part 02 - Support Vector Machines - K-Nearest Neighbors - Decision Trees and Random Forest - Decision Trees and Random Forest Part 02 - Naive Bayes Classifiers

Course content

11 modules · 88 lessons

01Section 1. INTRODUCTION5 lessons

1. Introduction and Welcome Message
2. Updates on Udemy Reviews
3. Course Overview
ML Classification Package
Quiz

02Section 2. What is Machine Learning The Big Picture3 lessons

4. What is Machine Learning The Big Picture Part 1
5. What is Machine Learning The Big Picture Part 2
Quiz

03Section 3. Installation & Setup[Optional] [Skip If You arae Familiar With Jupter Notebooks4 lessons

6. What is Anaconda and How to download it
7. What are Jupyter Notebooks
8. How to run a Jupyter Notebook
Quiz

04Section 4. Logistic Regression8 lessons

9. Logistic Regression Introduction and Learning Outcomes
10. Logistic Regression Intuition
11. Confusion Matrix Overview
12. Logistic Regression - Project 1 Project Overview
13. Logistic Regression Project 1 Loading Data
14. Logistic Regression Project 1 Visualization
15. Logistic Regression Project 1 Data Cleaning
16. Logistic Regression Project 1 Data Cleaning part 2

05Section 5. Logistic Regression Part 029 lessons

17. Logistic Regression Project 1 Training
18. Logistic Regression Project 1 Testing
19. Logistic Regression -Project 2 Overview
20. Logistic Regression Project 2 Importing data
21. Logistic Regression - Project 2 - Data visualization
22. Logistic Regression - Project 2 - Cleaning data
23. Logistic Regression - Project 2 - Training Testing
24. Logistic Regression - Project 2 - Testing Visualization
Quiz

06Section 6. Support Vector Machines15 lessons

25. Support Vector Machines Intro and Learning Outcomes
26. Support Vector Machines - Intuition
27. Support Vector Machines - Project #1 - Project Overview
28. Support Vector Machines - Project #1 - Importing data
29. Support Vector Machines - Project #1 - Data Visualization
30. Support Vector Machines - Project #1 - Training
31. Support Vector Machines - Project #1 - Testing
32. Support Vector Machines - Project #1 - Improvements 1
33. Support Vector Machines - Project #1 - Improvements 2
34. Project #2 Overview
35. Support Vector Machines - Project #2 - Data import and visualization
36. Support Vector Machines - Project #2 - Training and evaluating the model
37. Support Vector Machines - Project #2 - Improvements 1
38. Support Vector Machines - Project #2 - Improvements 2
Quiz

07Section 7. K-Nearest Neighbors11 lessons

39. K-Nearest Neighbors Intro and Learning Outcomes
40. K-Nearest Neighbors - Intuition
41. KNN - Project #1 - Project overview
42. KNN - Project #1 - Data import and cleaning
43. KNN - Project #1 - TrainingTesting
44. KNN - Project #1 - Model visualization
45. KNN - Project #2 Overview
46. KNN - Project #2 - Data Visualization
47. KNN - Project #2 - Training
48. KNN - Project #2 - Evaluation
Quiz

08Section 8. Decision Trees and Random Forest8 lessons

49. Decision Trees and Random Forest Intro and Learning Outcomes
50. Decision Trees - Intuition
51. Random Forest - Intuition
52. Decision Trees & Random Forest - Project #1 - Project Overview
53. Decision Trees & Random Forest - Project #1 - Importing data
54. Decision Trees & Random Forest - Project #1 - Visualization
55. Decision Trees & Random Forest - Project #1 - Feature Engineering 1
56. Decision Trees & Random Forest - Project #1 - Feature Engineering 2

09Section 9. Decision Trees and Random Forest Part 029 lessons

57. Decision Trees & Random Forest - Project #1 - Training
58. Decision Trees & Random Forest - Project #1 - Evaluation
59. Decision Trees & Random Forest - Project #1 - Improvements
60. Decision Trees & Random Forest - Project #2 Overview
61. Decision Trees & Random Forest - Project #2 - Overview and set up
62. Decision Trees & Random Forest - Project #2 - Data cleaning Model training
63. Decision Trees & Random Forest - Project #3 - TrainingTesting
64. Decision Trees & Random Forest - Project #2 - Evaluation
Quiz

10Section 10. Naive Bayes Classifiers15 lessons

65. Naive Bayes Intro and Learning Outcomes
66. Naive Bayes Intuition
67. Naive Bayes - Mathematics
68. Project #1 - Project overview
69. Project #1 - Data Visualization
70. Project #1 - Count vectorizer
71. Project #1 - Training part 1
72. Project #1 - Training part 2
73. Project #1 - Testing
74. Project #2 - Overview
75. Project #2 - Importing data
76. Project #2 - Training Testing
77. Project #2 - Testing
78. Project #2 - Improvements
Quiz

11Final Exam1 lessons

Final Exam

Your instructor

PACI

Punjab Artificial Intelligence and Cybersecurity Initiative

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PACI (Punjab Artificial Intelligence and Cybersecurity Initiative) publishes recorded courses in artificial intelligence, cybersecurity, web development and data skills.

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