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Machine learning is the future for the next generation of software professionals.

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Here is what you will Learn from Fundamentals of Machine Learning for Software Engineers

1. 1. Supervised Learning
2. The Math Behind Machine Learning
3. 2. Our First Learning Program
4. Get to Know the Problem
5. Coding Linear Regression
6. Training
7. Add a Bias
8. Playground (Tweak the Learning Rate)
9. Quiz: Basics of Machine Learning
10. 3. Walking the Gradient
11. The Limitations of Linear Regression
12. Gradient Descent
13. Partial Derivatives
14. Put Gradient Descent to the Test
15. Playground (Basecamp Overshooting)
16. 4. Hyperspace
17. Add More Dimensions
18. Matrix Math
19. Upgrade the Learner
20. Put It All Together
21. Playground (Field Statistician)
22. Quiz: The Gradient Descent
23. 5. A Discern Machine
24. Linear Regression Limitation
25. Invasion of the Sigmoids
26. Update the Gradient
27. Classification in Action
28. Playground (Weighty Decisions)
29. 6. Get Real
30. Data Comes First
31. Our Own MNIST Library
32. The Real Thing
33. Playground (Tricky Digits)
34. Quiz: A Discerning Machine and Getting Real
35. 7. The Final Challenge
36. Multi-class Classifier
37. One Hot Encoding
38. Decode the Classifier’s Answers
39. Launch the Classifier
40. Playground (Minesweeper)
41. 8. The Perceptron
42. Introduction to Perceptron
43. Where Perceptrons Fail
44. A Tale of Perceptrons
45. Quiz: The Perceptrons
46. 9. Designing the Network
47. Assembling a Neural Network from Perceptrons
48. Introduction to Softmax
49. 10. Building the Network
50. Code Forward Propagation
51. Writing the Algorithm (Softmax and Classification)
52. Cross Entropy
53. Playground (Time Travel Testing)
54. 11. Training the Network
55. The Case for Backpropagation
56. From the Chain Rule to Backpropagation
57. Apply Backpropagation
58. Initialize the Weights
59. Train the Network
60. Playground
61. Quiz: Design, Build and Train the Network
62. 12. How Classifiers Work
63. Trace a Boundary
64. Bend the Boundary
65. Playground
66. 13. Batchin’ Up
67. Learning of the Model
68. Introduction to Batch
69. Understand Batches
70. Playground (The Smallest Batch)
71. 14. The Zen of Testing
72. The Threat of Overfitting
73. The Development Cycle of Neural Networks
74. Playground (Thinking About Testing)
75. 15. Let’s Do Development
76. Preparing Data
77. Tune Hyperparameters
78. Tune Learning Rate and Batch Size
79. The Final Test
80. Playground (Achieving 99%-MNIST)
81. Quiz: Develop the Network
82. 16. A Deeper Kind of Network
83. The Echidna Dataset
84. Build a Neural Network with Keras
85. Keras in Action
86. Playground (Keras)
87. Quiz: A Deeper Kind of Network
88. 17. Defeating Overfitting
89. Overfitting Explained
90. Review of the Deep Network
91. Regularize the Model
92. A Regularization Toolbox
93. Playground (Keeping it simple)
94. Quiz: Defeat Overfitting
95. 18. Taming Deep Networks
96. Understand Activation Functions
97. Beyond the Sigmoid
98. Techniques to Improve Neural Network
99. Playground (The 10 Epochs Challenge)
100. Quiz: Tame Deep Networks
101. 19. Beyond Vanilla Networks
102. The CIFAR-10 Dataset
103. The Building Blocks of CNNs
104. Run on Convolutions
105. Playground (Hyperparameters Galore)
106. Quiz on Deep Networks
107. 20. Into the Deep
108. The Rise of Deep Learning
109. Unreasonable Effectiveness
110. Where Now?