Neural Networks

• Get familiar with the diagram and components of a neural network
• Understand the concept of a "layer" in a neural network
• Understand how neural networks learn new features.
• Understand how activations are calculated at each layer.
• Learn how a neural network can perform classification on an image.
• Use a framework, TensorFlow, to build a neural network for classification of an image.
• Learn how data goes into and out of a neural network layer in TensorFlow
• Build a neural network in regular Python code (from scratch) to make predictions.
• (Optional): Learn how neural networks use parallel processing (vectorization) to make computations faster.

Neural Network Training

• Train a neural network on data using TensorFlow
• Understand the difference between various activation functions (sigmoid, ReLU, and linear)
• Understand which activation functions to use for which type of layer
• Understand why we need non-linear activation functions
• Understand multiclass classification
• Calculate the softmax activation for implementing multiclass classification
• Use the categorical cross entropy loss function for multiclass classification
• Use the recommended method for implementing multiclass classification in code
• (Optional): Explain the difference between multi-label and multiclass classification

Advice to apply maching learning

• Evaluate and then modify your learning algorithm or data to improve your model's performance
• Evaluate your learning algorithm using cross validation and test datasets.
• Diagnose bias and variance in your learning algorithm
• Use regularization to adjust bias and variance in your learning algorithm
• Identify a baseline level of performance for your learning algorithm
• Understand how bias and variance apply to neural networks
• Learn about the iterative loop of Machine Learning Development that's used to update and improve a machine learning model
• Learn to use error analysis to identify the types of errors that a learning algorithm is making
• Learn how to add more training data to improve your model, including data augmentation and data synthesis
• Use transfer learning to improve your model's performance.
• Learn to include fairness and ethics in your machine learning model development
• Measure precision and recall to work with skewed (imbalanced) datasets

Decision Trees

• See what a decision tree looks like and how it can be used to make predictions
• Learn how a decision tree learns from training data
• Learn the "impurity" metric "entropy" and how it's used when building a decision tree
• Learn how to use multiple trees, "tree ensembles" such as random forests and boosted trees
• Learn when to use decision trees or neural networks