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. Channel Link: https://bit.ly/39YRQBK