# Machine Learning Algorithms Problem Types

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 long-term 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 non-linear 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