Types of problems we can solve with machine learning:
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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
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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
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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
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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
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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
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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
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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