Data Science for Beginners: Algorithms Used in Machine Learning
Data Science for Beginners: Algorithms Used in Machine Learning
Machine learning algorithms are very much part of the present and the future, as discussed over at runrex.com, as they help computers get smarter and more personal, helping them perform tasks as simple as playing chess or as complex as performing surgery. As a data scientist, learning the various machine learning algorithms is important as you can use the knowledge in your machine learning projects. This article will look to highlight the algorithms that are commonly used in machine learning, which will encompass all the three types of machine learning techniques: supervised learning, unsupervised learning, and reinforcement learning, all three of which are discussed in detail over at guttulus.com.
There are 10 commonly used machine learning algorithms, and they include:
- Linear Regression
When it comes to Linear Regression, a relationship is established between independent and dependent variables by having them fitted to a line, which is known as the regressions line and is represented by a linear equation Y= a *X + b where:
- Y is the Dependent Variable
- a is the slope
- X is the Independent Variable
- b is the intercept
To derive the coefficients of a and b, you minimize the sum of the squared difference of distance between the data point and the regression line, as explained over at runrex.com.
- Logistic Regression
This algorithm is used to estimate discrete values from a set of independent variables, and these discrete values are usually binary values like 0/1. Logistic Regression helps predict the probability of an event by fitting data to a logit function, which is why it is also called Logit Regression.
- Decision Tree
This is a supervised learning algorithm used for classifying problems and is one of the most popular machine learning algorithms used today according to the subject matter experts over at guttulus.com. It works well classifying for both continuous dependent and categorical variables. Here, the population is split into two or more homogenous sets based on the most significant attributes or independent variables.
- Support Vector Machine (SVM)
This is a method of classification where you plot raw data as points in an n-dimensional space, with n being the number of features that you have. The value of each of your features is then tied to a particular coordinate, which makes it easy to classify the data. You can then use lines called classifiers to split the data and plot them on a graph.
- Naïve Bayes
The assumption that this classifier takes, as explained over at runrex.com, is that the presence of a particular feature in a class is unrelated to the presence of any other feature. This means that even if these features are related to each other, a Naïve Bayes classifier would still consider all of these properties independently when calculating the probability of a particular outcome.
- K-Nearest Neighbors (KNN)
This algorithm is widely used within the data science industry to solve classification problems although it can be applied to both classification and regression problems. This simple algorithm stores all available cases and classifies any new cases by taking a majority votes of its k neighbors, after which the new case is assigned to the class with which it has the most in common. This measurement is performed by a distance function.
- K-Means
This is an unsupervised algorithm that is used to solve clustering problems. It is an iterative algorithm that groups similar data into clusters, after which it calculates the centroids of k clusters and assigns a data point to that particular clusters having the least distance between its centroid and the data point as explained over at guttulus.com.
- Random Forest
A Random Forest is a collection of decision trees. When looking to classify a new object based on its attributes, each tree is classified, and the tree, then, ‘votes’ for the class. The classification having the most votes is then chosen by the forest, with more details on how each tree is planted and grown to be found over at the brilliant runrex.com.
- Dimensionality Reduction Algorithms
Data science has gone mainstream in today’s world which means that huge amounts of data are being collected, stored, and analyzed by corporates, research organizations, and government agencies. While data scientists know that this raw data contains a lot of information, they also know that the challenge lies in identifying significant patterns and variables from it. This is where Dimensionality Reduction Algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest comes in as they can help you as a data scientist find relevant details from this raw data.
- Gradient Boosting and AdaBoost
These two, as explained over at guttulus.com, are boosting algorithms that are used when massive loads of data have to be handled to make highly accurate predictions. Boosting is an ensemble learning algorithm combining the predictive power of several base estimators to improve robustness. It, therefore, combines multiple weak or average predictors to build a strong predictor. You should consider these boosting algorithms when entering data science competitions like Kaggle, AV Hackathon, and CrowdAnalytix as they always work well in such competitions where they achieve accurate results when used along with Python and R Codes.
The above are the 10 most commonly used machine learning algorithms, and you can uncover more insights on this and other related topics over at the excellent runrex.com and guttulus.com.
