https://blog.adamzolo.com Blog about Things by Adam Zolotarev Mon, 18 Feb 2019 13:28:00 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.1 https://blog.adamzolo.com/machine-learning-algorithms-problem-types/ https://blog.adamzolo.com/machine-learning-algorithms-problem-types/#respond Sat, 21 Jul 2018 14:32:14 +0000 http://blog.adamzolo.com/?p=767 Continue reading →

]]> 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

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