Vector  Structured Learning  Unstructured Learning  Reinforced Learning 
What 
Needs Humans Learn relationships Needs explicit output 
explore data no explicit output 
perform a task get rewards 
When to use 
Classify data  humans define type of behaviour algorithms form new data sets 
No data classification Finds patterns with data set reclassification data to form new data 
No Training data NO end state Need to interact with model to derive output 
How to use 
Label all input data define output find connections between data sets apply algos to form new data sets 
Unlabeled data infers structures from data ID's groups of data with same behaviour 
Takes an action/task gets a reward optimzation based on best series of actions 
Various Algo's and business case
Supervised learning: Algorithms and sample business use cases
1 
Liner regression Highly interuptable . need to have past relationship identified for the output variable helps predicate future output variables 
understand sales drivers such as price, ad's or distribution optimzation of price points 
2 
Logistic regression used for classification tasks, meaning the output variable is binary (eg, only black or white) rather than continuous (eg, an infinite list of potential colors) 
Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc) 
3 
Linear/quadratic discriminant analysis a logistic regression to deal with nonlinear problems—those in which changes to the value of input variables do not result in proportional changes to the output variables 
Predict client churn Predict a sales lead’s likelihood of closing 
4 
Decision tree Highly interpretable classification or regression model that splits datafeature values into branches at decision nodes (eg, if a feature is a color, each possible color becomes a new branch) until a final decision output is made 
a decision framework for hiring new employees Understand product attributes that make a product most likely to be purchased 
5 
Naive Bayes allows the probability of an event to be calculated based on knowledge of factors that might affect that event (eg, if an email contains the word “money,” then the probability of it being spam is high) 
Analyze sentiment to assess product perception in the market Create classifiers to filter spam emails 
6 
Support vector machine typically used for classification but can be transformed to perform regression. It draws an optimal division between classes (as wide as possible). It also can be quickly generalized to solve nonlinear problems 
Predict how many patients a hospital will need to serve in a time period Predict how likely someone is to click on an online ad 
7 
Random forest model that improves the accuracy of a simple decision tree by generating multiple decision trees and taking a majority vote of them to predict the output, which is a continuous variable ( 
Predict call volume in call centers for staffing decisions Predict power usage in an electricaldistribution grid 
8 
AdaBoost Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome 
Detect fraudulent activity in creditcard transactions. Achieves lower accuracy than deep learning Simple, lowcost way to classify images (eg, recognize land usage from satellite images for climatechange models). Achieves lower accuracy than deep learning 
9 
Gradientboosting trees Classification or regression technique that generates decision trees sequentially, where each tree focuses on correcting the errors coming from the previous tree model. The final output is a combination of the results from all trees 
Forecast product demand and inventory levels Predict the price of cars based on their characteristics (eg, age and mileage) 
10 
Simple neural network Model in which artificial neurons (softwarebased calculators) make up three layers (an input layer, a hidden layer where calculations take place, and an output layer) that can be used to classify data or find the relationship between variables in regression problems 
Predict the probability that a patient joins a healthcare program Predict whether registered users will be willing or not to pay a particular price for a product
