In the present scenario where most of the tasks are performed automatically, thus, the definition of manual task is gradually changing. With the advancement in technology, we can foresee the advancement coming in the upcoming days. Machine learning is considered as a sub-division of artificial intelligence. It is basically the process that helps the computer perform their designated functions efficiently. It can help the computers play chess, perform surgeries and even can function depending on your personal requirement. One of the revolutionary changes is to witness how these computer tools and techniques have been regularized. So if you are a machine learning newbie, it is important for you to understand the basic machine learning algorithms that are used by data scientists.
Top 10 Machine Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision tree
- Support Vector Machine
- Naive Bayes
- KNN (K-Nearest neighbor)
- Random Forest
- Dimensionality reduction algorithms
- Gradient boosting and AdaBoost
Algorithm 1: Linear Regression
Linear Regression – In this particular algorithm, you can understand its function by visually placing the items in increasing order of their weight and eventually you reach on a conclusion. This is how linear regression works.
Algorithm 2: Logistic Regression
Logistic Regression – In this methodology, the most discrete value is estimated based on the set of predictor variables. It helps you predict an occurrence by putting in the data to a logit function. The different methods to improve this logistic regression model are:
- Technique of regularization
- Use of non-linear model
- Addition of interaction terms
- Feature of elimination
Algotithm 3: Decision tree
Decision Tree – This particular algorithm is popular among data scientists for identifying and differentiating the diverse issues. The support tool makes use of several trees lie graphs for making decisions, event outcomes, resource costs, and utilities. Then the population is split into two different homogenous set based on independent variables.
Algorithm 4: Support Vector Machine
Support Vector Machine – You can easily classify raw data with the help of this methodology in any dimensional area. Then the values of the individual coordinates are tied to a particular coordinate making it easy to differentiate the diverse sets of data.
Algorithm 5: Naive Bayes
Naive Bayes – This algorithm functions differently from the others as it performs based on the assumption that a particular characteristic is independent of the other one. Incase, there is an interrelation between the two features; the classifier will calculate the properties individually while reaching the outcome.
Algorithm 6: KNN (K-Nearest neighbor)
KNN (K-Nearest neighbor) -This particular algorithm is related to the process of regression however, it can be applied for both classification and for sorting problems In the world of data Science, it is more often used in solving the classification problems. It is an easy form of algorithm, in which it is capable of storing all the relevant available cases and classifies a new case by taking a maximum vote of its k neighbours.
Algorithm 7: K-Means
K-Means – This particular algorithm is best used for solving the clustering problems. The individual data sets are arranged in such a way so that the data points in the cluster are homogeneous or heterogeneous in nature from the data present in other clusters.
Algorithm 8: Random Forest
Random Forest – It is one of the popular forms of machine learning algorithms that classifies a new object based on its qualities. Collectively the decision trees are called the random forest. Here, each individual tree is sorted and then the tree vote for that particular class. Then the forest chooses the categorization based on maximum votes.
Algorithm 9: Dimensionality reduction algorithms
Dimensionality reduction algorithms – In several organizations such as government bodies, and other research organizations, large amount of data are analyzed and stored. So, with the help of dimensionality reduction algorithms such as factor analysis, decision tree, mission value, and random forest, all the important and relevant details can be gathered easily without any hassle.
Algorithm 10: Gradient boosting and Ada Boost
Gradient boosting and Ada Boost- These algorithms are used for handling a large amount of data with maximum accuracy and speed. Boosting is a process of improving the power and efficiency combining the power of the base estimators. In other words, it is a combination of all the weak and average predictors to build a strong one.
Here mentioned are just the basics of machine learning algorithms. If you want to make a career in the diverse areas machine learning such as data mining, data labeling, it is important to delve deep and gauge a better understanding of all the machine learning tools.