Types of problems we can solve with machine learning:

Regression helps establish a relationship between one or more sets of data
 Algorithms
 Simple linear regression
 Multiple Linear Regression
 Polynomial Regression
 Support Vector Machines (SVR)
 Decision Tree
 Random Forest Regression
 Sample problem: calculate the time I get to work based on the route I take and the day of the week

Classification – helps us answer a yes/no type of question based on one or more sets of data
 Algorithms
 K Nearest Neighbors (KNN)
 Kernel SVM
 Logistic Regression
 Naïve Bayes
 Decision Tree
 Random Forest Classification
 Sample problem: will I be late or on time based on the route I take and the day of the week

Clustering – helps us discover clusters of data
 Algorithms
 Hierarchical Clustering
 K Means
 Sample problem: classify the customers into specific groups based on their income and spending

Association – helps determine an association among multiple events
 Algorithms
 Apriori
 Eclat
 Sample problem: if I like movie A, what other movies will likely to enjoy

Reinforcement – helps to better exploit while exploring
 Algorithms
 Thomson Sampling
 UCB
 Sample problem: we want to determine the most effective treatment. Instead of conduction a longterm random trial, use UCB or Thompson Sampling to determine the best treatment in a shorter interval

Natural Language Processing
 Algorithms
 Any classification algorithm, but most popular are Naïve Bayes and Random Forest
 Sample problem: determine if an amazon review is positive or negative

Deep Learning – can help determine hard to establish nonlinear relationships between multiple input parameters and some expected outcome
 Algorithms
 Artificial Neural Networks (ANN)
 Convolutional Neural Networks (CNN) – especially helpful when processing images
 Sample problem: based on the credit score, age, balance, salary, tenure… determine if a customer is likely to continue using your service or leave