Before knowing about machine learning algorithms, let’s first know about machine learning. Machine learning is the concept of teaching machines to learn from their own.
Though it may sound like a risk for humans, it’s just the opposite. Machine learning simply means to teach a machine to perform repetitive tasks with high accuracy. Machine learning helps to build machines to perform such tasks, which are either risky or tiresome.
Machine learning is an essential part of our daily life such as face-detection in smartphones or the advertisements and recommendations by the social media platforms such as Facebook, Instagram, LinkedIn, etc., or the fraud transactions detection system in the banks or recommendations of product by the shopping sites such as Flipkart, Amazon, etc. Machine learning has created an immense impact on our lives.
What is Machine Learning Algorithm?
Machine Learning Algorithm is more evolved than a regular algorithm. It allows us to learn automatically from the data provided. Machine Learning Algorithm simply makes any program smarter.
Types of Machine Learning Algorithms –
Machine Learning Algorithms are generally classified into three categories –
- Supervised Learning – This machine learning algorithm comprises a target/outcome variable that needs to be predicted with the help of a given set of predictors. With the help of the variables, a function is generated for desired outputs. Until a desired level of accuracy is achieved on the training process, the process continues. Some examples of supervised learning are –Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc.
- Unsupervised Learning – This machine learning algorithm is mainly used for dividing the population into different groups. This algorithm doesn’t predict the target or outcome variable. Customers are segmented into different groups for a specific intervention. Some examples of unsupervised learning are – Apriori algorithm, K-means.
- Reinforcement Learning – This machine learning algorithm is trained to make specific decisions. Under this algorithm, the machine is exposed to the environment to train itself based on trial and error. This algorithm help machines to learn from their experience to make accurate business decisions. The example of reinforcement learning is – Markov Decision Process.
Some common Machine Learning Algorithms –
- Linear Regression – This algorithm is based on a continuous variable(s). The main purpose of linear regression is to estimate real values such as the total cost of a house, total sales, the total number of calls, etc.
- Logistic Regression – This is not a regression algorithm; rather, it is a classification. It is based on a given set of the independent variable(s). It is used for estimating separate values such as yes/no, true/false, 0/1, etc.
- Decision Tree – This algorithm is best suited for classification problems. It is a type of supervised learning algorithm. The categorical and continuous dependent variables, both works on it.
- SVM (Supply Vector Machine) – This is not an algorithm; rather, it’s a classification method. This is used to plot each data item as a point in n-dimensional space with the value of each feature be the value of a particular coordinate.
- Naïve Bayes – This is based on Bayes’ theorem. It is a classification technique used with an assumption of independence from predictors. In simple words, it assumes a particular feature in a class that is unrelated to any other feature’s presence.