4 algorithms machine learning engineers should know

Machine learning has made spectacular advancements in the past few years. Algorithms for machine learning are designed in such a way that they seamlessly blend artificial intelligence with true human intelligence to deliver the highest level of accuracy and understanding.

Over the years, machine learning systems have evolved to mimic the pattern-matching that human brains perform. Algorithms for machine learning are developed in a manner to teach computers how to recognize features of an object. For example, a computer is shown a cat and told that it is a cat. The computer then uses that information to classify the various characteristics of a cat and builds new information upon the existing data available. At first, a computer might classify a cat as an object with four legs and a tail, but later when a deer is introduced, it may identify the cat as a pet animal or based on its diet. As more animals are introduced, the classification gets more detailed and accurate. Because of such machine learning algorithms, a computer is able to continually modify its model based on new information. Thus, the computer can assign a predictive value to each model, which indicates the degree of confidence that an object is one thing over another. For example, yellow is a more predictive value for a banana than green is for a leafy vegetable.

In this blog post, I have listed down some commonly used machine learning algorithms. These algorithms can help in solving almost any data problem.

ALGORITHMS FOR MACHINE LEARNING 1: LINEAR REGRESSION

Linear regression helps in estimating real values based on a continuous variable. In this algorithm, you establish a relationship between an independent and dependent variable by fitting the best line. This best fit line is also known as the regression line. This regression line is represented by a linear equation Y= a *X + b. The components of this equation include:

• Y – Dependent Variable
• a – Slope
• X – Independent variable
• b – Intercept

Coefficients a and b are derived by minimizing the sum of squared difference of distance between data points and regression line.

Linear Regression consists of two types: Simple Linear Regression and Multiple Linear Regression. Simple Linear Regression includes one independent variable, while Multiple Linear Regression is characterized by multiple independent variables.

ALGORITHMS FOR MACHINE LEARNING 2: DECISION TREE

A decision tree is a type of supervised learning algorithm that is mostly used for classification problems. It can work for both categorical and continuous dependent variables. In this algorithm, the sample population is split into two or more homogeneous sets. This classification is based on most significant attributes of variables for making as many distinct groups as possible.

ALGORITHMS FOR MACHINE LEARNING 3: SVM (SUPPORT VECTOR MACHINE)

In this machine learning algorithm, you can plot each data item as a point in an n-dimensional space and the value of each feature is represented by the value of a particular coordinate.

For example, if you only have two features like height and weight of an individual, you can first plot these two variables in a two-dimensional space. In this two-dimensional space, each point will have two coordinates, known as support vectors. Now, you should find a line that splits the data between the two differently classified groups of data. This line will be such that the distances from the closest point in each of the two groups will be farthest away.

ALGORITHMS FOR MACHINE LEARNING 4: NAIVE BAYES

This is a classification algorithm based on Bayes’ theorem with an assumption of independence between predictors. In simple words, a Naive Bayes classifier assumes that the presence of a particular feature in a class is not related any other feature. For example, a fruit may be considered to be a banana if it is yellow, elongated, and about 6 inches in length. Even if these features typically depend on each other, a naive Bayes classifier will consider all of these properties to be independent and contribute separately to the probability that this fruit is a banana.

Machine learning has varied practical applications that help in driving real business results, including time and money savings. Machine learning implementation will provide you with the potential to dramatically impact the future of your organization. These algorithms will help you in complicated decision-making thereby, providing the correct solution.