Machine learning algorithms are the only right formula to train the model and develop the right AI application that can predict with accuracy. Hence, selecting the right algorithm is important to ensure your machine learning model work properly.
Actually, choosing the right algorithm helps you to determine the right goal of your ML model. Hence, understanding the machine learning model algorithms is important for machine learning engineer to choose the right model type. Here we brought, types of machine learning algorithms you should know.
TYPES OF MACHINE LEARNING ALGORITHMS
#1 Linear Regression
Linear regression is one of the most popular type of machine learning algorithm used in training such models. Linear regression algorithms is simply a correlations between two variables in a set of data. A set of inputs and their proportionate outputs are examined and quantified to show relationship, the change in one variable impacts the other.
Due to its simplicity, Linear regression is very popular and this algorithm is easily interpretable and don’t need parameter tuning or almost transparent. And this type of algorithm is used in forecasting the sales and assessment of risk for organization seeking to make the long-term business decisions with highest accuracy.
#2 Decision Tree
Decision tree algorithm utilize the data and graphs to show the possible result of variety of decisions. Decisions trees categorize response variables and predict results variables as per the learn from the past decisions.
The best part of decision tree is that it is visual and easy communicated process of decision making and results are relatively accessible to data scientists. And you need to be able to transom decisions and check how they affects the results.
Decision is popular because of capability to outline multiple outcomes and test without any further need of multiple algorithms deployment by data scientists.
#3 Support Vector Machines
Support vector machines or SVM is another machine learning algorithm that internally analyzes the data sets into various classes helping with future classifications.
Technically, speaking, SVM discovers a line that separates training data into specific classes and increase the margins of every class in order to generalize future data into right classes for accurate predictions.
Companies having nonlinear data and different kinds of data sets are looking to SVM and automated services to help classify images. As this algorithm works best for training data that can be separated clearly by line which is also known as hyperplane. Nonlinear data can be programmed into a feature of SVM called nonlinear SVMs.
SVMs are mainly used in the financial sector, as they give highly accuracy on both current and future data sets. The algorithms can be used to compare relative financial values, performance and investment gains on virtual basis.
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#4 K-means Clustering
K-means algorithm repetitive method of grouping the data sets through characterized clusters and putting aforesaid clusters with input data connected and sectioned. In a K-cluster algorithm is known for accurate and streamlined groupings processed in a comparatively for short period time, compare to other algorithms.
K-means clustering is mainly popular among search engines to produce relevant information and enterprises looking to group searchers intent by indicative meaning. This one of the types of machine learning algorithms that requires not that much training data compare to other algorithms.
#5 Apriori
The Apriori algorithms is another algorithms based on the Apriori principle is another most commonly used in market basket analysis to exploit item sets and create association rules. The algorithms helps to find out the correlation between A and B in a data set to produce a positive or negative correlation between products.
The Apriori algorithm is suitable for sales filed that are seeking to notice that products customers are intent to buy in the combination with other products. Apriori algorithms are also used by e-commerce companies including online search engines like Google and Bing to predict the results using the search relevance algorithm.
These are the kinds of machine learning algorithms can be used to develop the AI-based models work for a particular industry. But choosing the right machine learning training data for AI such algorithm is also important to make sure the model work properly and give the precise predictions with best level of accuracy.
