Linear Regression and Its Applications in Machine Learning

March 19, 2019
Machine Learning Certification

 

Machine Learning needs to be supervised for the computers to effectively and efficiently utilize their time and efforts. One of the top ways to do it is through linear regression and here’s how. 

Even the most careful managers can make mistakes in organizations. But today, we live in a world where automation is powering most industries, thereby reducing cost, increasing efficiency and eliminate human error.  This is dominated by the rising application of machine learning and artificial intelligence. So, what gives machines the ability to learn and understand large volumes of data? It is through the learning methodologies such as linear regression. 

So, what is linear regression? Simply put, machines need to be supervised in order to effectively learn new things. Linear regression is a machine learning algorithm that enables this. The biggest ability of machines is that they can learn about the problem and execute solutions seamlessly. This greatly reduces and eliminates human error.  It is also used to find the relationship between forecasting and variables. A task is performed based on a dependable variable by analyzing the impact of an independent variable on it. Those proficient in programming software such as Python, C can sci-kit learn library to import the linear regression model or create their own custom algorithm before applying it to the machines. This means that it is highly customizable and easy to learn. Organizations across the world are heavily investing in linear regression training when it comes to their employees in order to prepare the workforce for the future. 

Top benefits of linear regression in machine learning is as follows.

Forecasting

A top advantage of using a linear regression model in machine learning is the ability to forecast trends and make predictions which are feasible. Data scientists can use these predictions and make further deductions based on machine learning. It is quick, efficient and accurate. This is predominantly since machines process large volumes of data and there is minimum human intervention. Once the algorithm is established, the process of learning becomes simplified. 

Beneficial to small businesses

By altering one or two variables, machines can understand the impact on sales. Since deploying linear regression is cost-effective, it is greatly advantageous to small businesses since short- and long-term forecast can be made when it comes to sales. This means that small businesses can plan their resources well and create a growth trajectory for themselves. They will also be to understand the market and its preferences and learn about supply and demand.

Preparing Strategies

Since machine learning enables prediction, one of the biggest advantages of a linear regression model in it is the ability to prepare a strategy for a given situation, well in advance and analyze various outcomes. Meaningful information can be derived from the regression model of forecasting thereby helping companies plan strategically and make executive decisions. 

Conclusion

Linear regression is one of the most common machine learning processes in the world and it helps prepare businesses in a volatile and dynamic environment.

Post a comment

20 + 18 =