Most Machine Learning courses start with Linear Regression
| Algorithm | Type | Uses |
|---|---|---|
| k-Nearest Neighbor | Supervised Clustering | Classifying items based on the most common of the k-nearest neighbors |
| k-Means | Unsupervised Clustering | Classifying items into one of k groups |
| Recommender | Clustering | Picking Movies/Foods/Music based on either (A) Your prior favorites or (B) prior favorites of people like you. |
| Neural Networks | Linear Regression | Finding the price of a house based on neighborhood and square footage. |
| Neural Networks | Logistic Regression | Finding the probability a tumor with certain size, proteins and shape is cancerous. |
| Convolutional Neural Networks | Logistic Regression | Finding the probability that picture is of a dog, or cat, or house. |
| Recurrent Neural Network | Logistic Regression | Finding a probability where there is a prior state dependence (i.e. not just based on immediate inputs). |
| Restricted Boltzman Machine | Unsupervised | Finding hidden relationships in data. |
| AutoEncoders | Unsupervised Regression | Encoding a picture down to a certain number of bytes (say, 30) then re-encoding it back. Good for identifying essential dimensions of an image. |
- Source Data manipulation (i.e. transforming a list of strings into numbers, doing one-hot encoding, etc).
- Model (LR, kNN, Neural Network)
- Width/Depth of Neural network model or k-value.
- Activation functions (sigmoid, tanh, ReLU)
- Distance Functions (distance between elements in a kNN model)
- Cost/Error Functions
- Epoch count and Learning Rate
Talking Computers