# Machine learning (ML) brains, while AI tech is the body. Here are some uses of ML: Forecasting, Segmentation, Fraud Detection, Classification
# Many types of algroithms, here are some: Deep Learning, Ensemble, Neural Networks, Regression, Regularization, BAyesian
# Decision trees, Dimension Reduction, CLustering and many more.
# In the subclasses of
# Regression - Linear, Logistic
# Bayesian - Naive Bayes (Gaussian, Bernoulli, Multinoimal)
# Dimension Reduction - Principal Component Analysis (PCA)
# Instance Based - k-NN, 
# Clustering - K-Means, Hierarchial CLustering, Density-Based SPatial Clustering of Applications with Noise (DBSCAN)

# Terminology
# Feature = Variable, column, attribute, field
# Instance = Row, Observation, Data point, value, case
# Target = predictant, dependent variable
# Data = predictor, independent variable

# In ML we use data sets. Random sampling of data breaks into test and training sets. General rule 2/3 of data is 
# use to train, 1/3 is used to test

# Supervised vs Unsupervised.
# Supervised makes predictions from labeled data. E.G., Spam detections - We start declaring what is spam
# Unsupervised makes predictions from unlabeled fata. E.G., House in market - We look at old data to find out price of new home

