Machine Learning Approaches

Most Machine Learning courses start with Linear Regression

Algorithm	Type	(Possible) 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.

Building a ML model is often a trial and error- involves properly selecting:

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

Possible Projects

Surveillance - using an Autoencoder or RBM to encode pictures of streets/buildings/cars to reduce data consumption and detect motion.
Old Pictures- using an autoencoder to store pictures of older pictures