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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. Supervised Learning
- The Math Behind Machine Learning
- 2. Our First Learning Program
- Get to Know the Problem
- Coding Linear Regression
- Training
- Add a Bias
- Playground (Tweak the Learning Rate)
- Quiz: Basics of Machine Learning
- 3. Walking the Gradient
- The Limitations of Linear Regression
- Gradient Descent
- Partial Derivatives
- Put Gradient Descent to the Test
- Playground (Basecamp Overshooting)
- 4. Hyperspace
- Add More Dimensions
- Matrix Math
- Upgrade the Learner
- Put It All Together
- Playground (Field Statistician)
- Quiz: The Gradient Descent
- 5. A Discern Machine
- Linear Regression Limitation
- Invasion of the Sigmoids
- Update the Gradient
- Classification in Action
- Playground (Weighty Decisions)
- 6. Get Real
- Data Comes First
- Our Own MNIST Library
- The Real Thing
- Playground (Tricky Digits)
- Quiz: A Discerning Machine and Getting Real
- 7. The Final Challenge
- Multi-class Classifier
- One Hot Encoding
- Decode the Classifier’s Answers
- Launch the Classifier
- Playground (Minesweeper)
- 8. The Perceptron
- Introduction to Perceptron
- Where Perceptrons Fail
- A Tale of Perceptrons
- Quiz: The Perceptrons
- 9. Designing the Network
- Assembling a Neural Network from Perceptrons
- Introduction to Softmax
- 10. Building the Network
- Code Forward Propagation
- Writing the Algorithm (Softmax and Classification)
- Cross Entropy
- Playground (Time Travel Testing)
- 11. Training the Network
- The Case for Backpropagation
- From the Chain Rule to Backpropagation
- Apply Backpropagation
- Initialize the Weights
- Train the Network
- Playground
- Quiz: Design, Build and Train the Network
- 12. How Classifiers Work
- Trace a Boundary
- Bend the Boundary
- Playground
- 13. Batchin’ Up
- Learning of the Model
- Introduction to Batch
- Understand Batches
- Playground (The Smallest Batch)
- 14. The Zen of Testing
- The Threat of Overfitting
- The Development Cycle of Neural Networks
- Playground (Thinking About Testing)
- 15. Let’s Do Development
- Preparing Data
- Tune Hyperparameters
- Tune Learning Rate and Batch Size
- The Final Test
- Playground (Achieving 99%-MNIST)
- Quiz: Develop the Network
- 16. A Deeper Kind of Network
- The Echidna Dataset
- Build a Neural Network with Keras
- Keras in Action
- Playground (Keras)
- Quiz: A Deeper Kind of Network
- 17. Defeating Overfitting
- Overfitting Explained
- Review of the Deep Network
- Regularize the Model
- A Regularization Toolbox
- Playground (Keeping it simple)
- Quiz: Defeat Overfitting
- 18. Taming Deep Networks
- Understand Activation Functions
- Beyond the Sigmoid
- Techniques to Improve Neural Network
- Playground (The 10 Epochs Challenge)
- Quiz: Tame Deep Networks
- 19. Beyond Vanilla Networks
- The CIFAR-10 Dataset
- The Building Blocks of CNNs
- Run on Convolutions
- Playground (Hyperparameters Galore)
- Quiz on Deep Networks
- 20. Into the Deep
- The Rise of Deep Learning
- Unreasonable Effectiveness
- Where Now?