Practical Machine Learning by Example in Python

Topic: Practical Machine Learning by Example in Python

Course content

Section 1: Course Structure and Development Environment
1. Course Structure and Development Environment
2. Course Quick Tips
3. Introduction to Jupyter Notebook
4. Jupyter notebook: Text Cells
5. Jupyter notebook: Code Cells
6. Jupyter notebook: Math Markup and Magic Commands
7. Sharing Colab Notebooks
8. Artificial Intelligence, Machine Learning, and Deep Learning
9. What you learned in this section

Section 2: Python Quick Start
10. About this section
11. Basic Syntax
12. String formatting
13. Literal string interpolation
14. Type conversion
15. Flow control
16. Lists

Assignment 3: Dot product
17. Dictionaries
18. Defining functions
19. Classes
20. File I/O and Modules
21. Prompting for passwords
22. What you learned in this section

Section 3: Example: Logistic Regression
23. The problem
24. Machine Learning Development Process
25. Data analysis
26. The model
27. The forward function
28. Loss and cost functions
29. Gradient descent
30. Backpropagation
31. Model training
32. Making predictions
33. Test vs. train accuracy
34. Speeding up training
35. Improving the model
36. What you learned in this section

Section 4: Foundations: NumPy
37. What is NumPy and why it is needed?
38. Creating data with NumPy
39. Basic operations

Assignment 9: Experiment with NumPy
40. Introduction to Linear Regression
41. Linear Regression Example
42. More Complex Models
43. Statistics and linear algebra
44. Visualizing data
45. Images
46. Reshaping data
47. What you learned in this section

Section 5: Foundations: Tensorflow
48. About this section
49. Model example
50. Model layers
51. Activation functions
52. Training example
53. Loss functions
54. Optimizers
55. Prediction example
56. Saving and restoring models
57. The Three Body Problem
58. What you learned in this section

Section 6: Example: Image recognition
59. The problem
60. Data analysis
61. Model selection
62. Data preparation
63. CNN Model Layers
64. Model definition
65. Model training
66. Making predictions
67. Error analysis
68. Hyperparameter tuning
69. Hyperparameter tuning example
70. Common questions
71. What you learned in this section

Section 7: Foundations: Pandas
72. What is Pandas and why is it useful?
73. Loading and inspecting data example
74. Indexing and selecting data example

Assignment 20: Experiment with Pandas
75. Sorting and transforming data example
76. Aggregations example
77. Visualizing data
78. What you learned in this section

Section 8: Example: Recommendations
79. The problem
80. Data analysis
81. Model selection
82. Data preparation
83. Embedding layers
84. Model definition
85. Model training
86. Predictions
87. Making predictions
88. Error analysis
89. Common questions
90. What you learned in this section

Section 9: Example: Sentiment Analysis
91. The Problem
92. Data Analysis
93. Supervised Learning
94. Data Preparation
95. Model Definition
96. Model Training
97. Transfer Learning with BERT
98. Transfer Learning Example
99. Fine Tuning and Prediction
100. What you learned in this section

Section 10: Example: Fraud detection
101. The problem
102. Data analysis
103. Unsupervised learning
104. Data preparation
105. Model definition
106. Model training
107. Making predictions
108. Common questions
109. What you learned in this section

Section 11: Next steps
110. Next steps
111. Thank you

Share to help us.

[aviudara]

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[assiaben]

very clear explanation and really really helpful, thanks Vabs Tutorials

[lemidemesew]

why did you stop making vidoes can you get the video the complete penetratrion testing bootcamp please