Machine Learning
What is machine learning?
“Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions”
Classification of Machine Learning
1.) Supervised Learning
Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.
It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.
Supervised learning problems can be further grouped into regression and classification problems.
- Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
- Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
Some common types of problems built on top of classification and regression include recommendation and time series prediction respectively.
Some popular examples of supervised machine learning algorithms are:
- Linear regression for regression problems.
- Random forest for classification and regression problems.
- Support vector machines for classification problems.
2.)Unsupervised Machine Learning
Unsupervised learning is where you only have input data (X) and no corresponding output variables.
The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
Unsupervised learning problems can be further grouped into clustering and association problems.
- Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
- Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
Some popular examples of unsupervised learning algorithms are:
- k-means for clustering problems.
- Apriori algorithm for association rule learning problems.
3.) Reinforcement Learning
Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.
In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions
Types of Reinforcement: There are two types of Reinforcement:
- Positive –Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words, it has a positive effect on behavior.
- Negative –Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided.
