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Machine Learning Regression Masterclass

New: Published in the last 30 days.

Machine Learning Regression Masterclass: a self-paced PACI course with 81 lessons in 11 sections and a quiz after each section.

  • Machine Learning
  • English language
  • Beginner
No reviews yetPPACI

What you’ll learn

  • Section. 1 Itroduction to the course
  • Section. 2 Anaconda and jupyter installation
  • Section. 3 simple linear regression
  • simple linear regression Part 02
  • Section. 5 Regression key performance indicators
  • Section. 6 Polynomial Regression

About this course

Machine Learning Regression Masterclass is a self-paced recorded course from the Punjab Artificial Intelligence and Cybersecurity Initiative (PACI). It has 81 lessons in 11 sections (70 video lessons, 10 quizzes, 1 reading / resource lessons). What the course covers: - Section. 1 Itroduction to the course - Section. 2 Anaconda and jupyter installation - Section. 3 simple linear regression - simple linear regression Part 02 - Section. 5 Regression key performance indicators - Section. 6 Polynomial Regression - Section. 7 Multiple linear regression - Section. 8 Logistic regression - Section. 9 Apply artificial neural networks to perform regression - Section. 10 Lasso and ridge regression

Course content

11 modules · 81 lessons

01Section. 1 Itroduction to the course5 lessons
  • 1. Course Welcome Message
  • 2. Updates on Reviews
  • 3. ML vs. DL vs. AI
  • ML Regression Course Package
  • Quiz 1
02Section. 2 Anaconda and jupyter installation3 lessons
  • 4. Download and Set up Anaconda
  • 5. What is Jupiter Notebook
  • Quiz 2
03Section. 3 simple linear regression7 lessons
  • 6. Intro to Simple Linear Regression
  • 7. Simple Linear Regression Intuition
  • 8. Least Squares
  • 9. Project #1 - Overview
  • 10. Project #1 - Data Visualization
  • 11. Project #1 - Divide Data into Training and Testing
  • 12. Project #1 - Train Model
04Section 4. simple linear regression Part 028 lessons
  • 13. Project #1 - Test Model
  • 14. Project #2 - Solution
  • 15. Project #2 - Solution
  • 16. Project #2 - Visualization
  • 17 . Project #2 - Prepare Training and Testing Data
  • 18. Project #2 - Test Model
  • 19. Project #2 - Model Testing
  • Quiz 3
05Section. 5 Regression key performance indicators5 lessons
  • 20. Regression Metrics Intro
  • 21. Regression Metric Part 1
  • 22. Regression Metric Part 2
  • 23. Bias Variance Tradeoff
  • Quiz 4
06Section. 6 Polynomial Regression12 lessons
  • 24. Polynomial Regression Intro
  • 25. Polynomial Regression - Intuition
  • 26. Poly Regression - Salary Load Data
  • 27. Poly Regression - Visualize Data
  • 28. Poly Regression - Linear Trainingtesting
  • 29. Poly Regression - Poly Part 1
  • 30. Poly Regression - Poly Part 2
  • 31. Poly Regression Project 2 Overview
  • 32. Poly Regression - Economies Linear -1
  • 33. Poly Regression - Economies Linear -2
  • 34. Poly Regression - Economies Poly
  • Quiz 5
07Section. 7 Multiple linear regression12 lessons
  • 35. Multiple Linear Regression Overview
  • 36. Project #1 - Load Data and Libraries
  • 37. Project #1 - Data Visualization
  • 38. Project #1 - Model Training and Evaluation
  • 39. Project #1 - Model Results Evaluation
  • 40. Project #2 - Overview
  • 41. Project #2 - Load Data
  • 42. Project #2 - Data Visualization
  • 43. Project #2 - Train the Model
  • 44. Project #2 - Model Evaluation
  • 45. Project #2 - Retraining Model
  • Quiz 6
08Section. 8 Logistic regression9 lessons
  • 46. Logistic Regression Intro
  • 47. Logistic Regression Intuition
  • 48. Confusion Matrix
  • 49. Project #2 - Data Import
  • 50. Project #2 - Visualization
  • 51. Project #2 - Data Cleaning
  • 52. Project #2 - Training Testing
  • 53. Model Testing Visualization
  • Quiz 7
09Section. 9 Apply artificial neural networks to perform regression14 lessons
  • 54. Theory Part 1
  • 55. Theory Part 2
  • 56. Theory Part 3
  • 57. Theory Part 4
  • 58. Theory Part 5
  • 59. Theory Part 6
  • 60. Project - Load Dataset
  • 61. Project - Visualize Dataset
  • 62. Scale the Data
  • 63. Train the Model
  • 64. Evaluate the Model
  • 65. Multiple Linear regression
  • 66. Model Improvement with more features
  • Quiz 8
10Section. 10 Lasso and ridge regression5 lessons
  • 67. Ridge Lasso Part 1
  • 68. Ridge Lasso Part 2
  • 69. Ridge Lasso Part 3
  • 70. Ridge and Lasso in Practice
  • Quiz 9
11Final Exam1 lessons
  • Final Exam

Your instructor

P
PACI

Punjab Artificial Intelligence and Cybersecurity Initiative

No reviews yet20 courses on NSDP

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

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