Introduction to Types of Machine Learning
The following article provides an outline for Types of Machine Learning. Machine learning is the subfield of AI that focuses on the development of the computer programs which have access to data by providing a system with the ability to learn and improve automatically.
For example, finding patterns in the database without any human interventions or actions is based upon the data type, i.e., labeled or unlabelled and based upon the techniques used for training the model on a given dataset. Machine learning is further classified as Supervised, Unsupervised, Reinforcement, and Semi-Supervised Learning algorithms; all these types of learning techniques are used in different applications.
What is Machine Learning?
Machine learning is a small application area of Artificial Intelligence in which machines automatically learn from the operations and finesse themselves to give better output. Based on the data collected, the machines improve the computer programs aligning with the required output. Owing to this ability of a machine to learn on its own, explicit programming of these computers isn’t required.
However, it has already seeped into our lives everywhere without us knowing. Practically every machine we use and the advanced technology machines we are witnessing in the last decade has incorporated machine learning to enhance the quality of products. Some examples of machine learning are self-driving cars, advanced web searches, speech recognition.
Difference Between Conventional Programming and Machine Learning
Conventional programming = Logic is programmed + Data is inputted + Logic gets run on the data + Output
Machine Learning = Data is inputted + Expected output is inputted + Run it on the machine for training the algorithm from input to output; in short, let it create its own logic to reach from input to output + Trained algorithm used on test data for prediction.
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Machine Learning Methods
We have four main types of Machine learning Methods based on the kind of learning we expect from the algorithms:
1. Supervised Machine Learning
Supervised learning algorithms are used when the output is classified or labeled. These algorithms learn from the past data that is inputted, called training data, runs its analysis and uses this analysis to predict future events of any new data within the known classifications. The accurate prediction of test data requires large data to have a sufficient understanding of the patterns. The algorithm can be trained further by comparing the training outputs to actual ones and using the errors for modification of the algorithms.
Real-Life Example:
- Image Classification – The algorithm is drawn from feeding with labeled image data. An algorithm is trained, and it is expected that the algorithm classifies it correctly in the case of the new image.
- Market Prediction – It is also called Regression. Historical business market data is fed to the computer. Then, with analysis and regression algorithm, the new price for the future is predicted depending on variables.
2. Unsupervised Machine Learning
Unsupervised learning algorithms are used when we are unaware of the final outputs, and the classification or labeled outputs are not at our disposal. These algorithms study and generate a function to describe completely hidden and unlabelled patterns. Hence, there is no correct output, but it studies the data to give out unknown structures in unlabelled data.
Real-Life Example:
- Clustering – Data with similar traits are asked to group together by the algorithm; this grouping is called clusters. These prove helpful in the study of these groups, which can be applied to the entire data within a cluster more or less.
- High Dimension Data – High dimension data is normally not easy to work with. With the help of unsupervised learning, visualization of high dimension data becomes possible.
- Generative Models – Once your algorithm analyses and comes up with the probability distribution of the input, it can be used to generate new data. This proves to be very helpful in cases of missing data.
3. Reinforcement Machine Learning
This type of machine learning algorithm uses the trial and error method to churn out output based on the highest efficiency of the function. The output is compared to find out errors and feedback fed back to the system to improve or maximize its performance. The model is provided with rewards which are basically feedback and punishments in its operations while performing a particular goal.
4. Semi-Supervised Machine Learning
These algorithms normally undertake labeled and unlabeled data, where the unlabelled data amount is large as compared to labeled data. As it works with both and in between supervised and unsupervised learning algorithms, therefore is called semi-supervised machine learning. Systems using these models are seen to have improved learning accuracy.
Example – An image archive can contain only some of its data labeled, e.g. Dog, cat, mouse, and a large chunk of images remain unlabelled.
Models based on the Kind of Outputs from the Algorithms
Below are the types of Machine learning models based on the kind of outputs we expect from the algorithms:
1. Classification
There is a division of classes of the inputs; the system produces a model from training data wherein it assigns new inputs to one of these classes. It falls under the umbrella of supervised learning. A real-life example can be spam filtering, where emails are the input that is classified as “spam” or “not spammed”.
2. Regression
Regression algorithm also is a part of supervised learning, but the difference being that the outputs are continuous variables and not discrete.
Example – Predicting house prices using past data.
3. Dimensionality Reduction
This type of Machine Learning is related to analyses of inputs and reduces them to only relevant ones for model development. Feature selection, i.e. input selection and feature extraction, is further topics needed to better understand dimensionality reduction.
On the basis of the above different approaches, there are various algorithms to be considered. Some very common algorithms being Linear and Logistic Regression, K-nearest neighbors, Decision trees, Support vector machines, Random Forest, etc. With the help of these algorithms, complex decision problems can have a sense of direction based on a huge amount of data. In order to attain this accuracy and opportunities, added resources, as well as time, are required to be provided. Therefore, machine learning used along with Artificial intelligence and other technologies is more effective to process information.
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