Machine learning is a subset of artificial intelligence (AI) and computer science that refers to the use of data and algorithms to imitate the way that humans learn, continuously improving its accuracy.
Today, every other app and software all over the Internet uses machine learning in some form or the other.
Types of Machine Learning
There are some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose.
ML algorithms help to solve different business problems like Regression, Classification, Forecasting, Clustering, Associations, etc.
Machine learning involves showing a large volume of data to a machine so that it can learn and make predictions, find patterns, or classify data. The algorithm used determines the machine learning type, which works a bit differently.
- Supervised Learning
Supervised learning refers to the use of labeled datasets to train algorithms that classify data or predict outcomes accurately. In this type, the machine learning algorithm is trained on labeled data. Although the data needs to be labeled accurately for this method to work, it is a powerful method.
Supervised learning helps organizations solve a variety of real-world problems at scale, such as predicting House Prices or Cars, and classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, naïve Bayes, linear regression, logistic regression, random forest, support vector machine, and more. The ML algorithm could be given a small training dataset that is a part of the bigger one to achieve a basic idea of the problem, solution, and data points. The algorithm then finds relationships between the parameters given. At the end of the training, the algorithm has an idea of how the data works and the relationship between the input and the output.
2. Unsupervised Learning
While supervised learning requires users to help the machine, unsupervised learning doesn’t use the same labeled training sets and data. In supervised learning, the labels allow the algorithm to find the exact nature of the relationship between any two data points. However, unsupervised learning does not have labels to work off of, resulting in the creation of hidden structures. The algorithm perceives relationships between data points abstractly, with no input required from human beings.
Unsupervised machine learning holds the advantage of being able to work with unlabeled data. The main aim of the unsupervised learning algorithm is to group or categorize the unsorted dataset according to the similarities, patterns, and differences. Machines are instructed to find the hidden patterns from the input dataset.
3. Semi-supervised Learning
Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.
We can imagine these algorithms with an example. Supervised learning is where a student is under the supervision of an instructor at home and college. Further, if that student is self-analyzing the same concept without any help from the instructor, it comes under unsupervised learning. Under semi-supervised learning, the student has to revise himself after analyzing the same concept under the guidance of an instructor at college.
4. Reinforcement Learning
Reinforcement machine learning is a behavioral machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.
It aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. The reinforcement learning algorithm continuously iteratively learns from the environment. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states.
In typical reinforcement learning use cases, such as finding the shortest route between two points on a map, the solution is not an absolute value. Instead, it takes on a score of effectiveness, expressed in a percentage value. The higher this percentage value is, the more reward is given to the algorithm. Thus, the program is trained to give the best possible solution for the best possible reward.
