• Washington
  • robinwcc2@gmail.com

Machine learning defines a set of methodologies and tools that allow computers to learn things of their own. It usually studies a pattern of sample inputs, and the algorithm can perform tasks based on this pattern. Machine learning offers a remunerative career and promises to solve intricate problems and make better predictions.  It can also foresee specific patterns and trends that are not recognizable by humans. Businesses use this feature of machine learning to understand customer behaviors and purchase preferences.

The efficiency and accuracy of machine learning algorithms improve as it processes more volumes of data. Machine learning algorithms are perfect for processing multi-dimensional data. It is a perfect tool to work around dynamic environments. Machine learning will play a substantial role in the development of modern systems and technologies. 


Importance of Machine Learning

Undergraduate and postgraduate candidates looking to pursue a career in this field will have huge scope in the future. Machine learning has become a part of almost every possible domain. Processes like cybersecurity, medicine, image recognition, facial recognition are largely dependent on machine learning algorithms. Besides, modern businesses are adopting business intelligence tools based on machine learning. 

Best Machine learning institute in Delhi can help working professionals learn from top industry mentors. These courses also provide hands-on experience and projects to help candidates learn about the practical application of machine learning. 


 Rank prediction with Machine learning

Machine learning helps in predicting page rankings after the application of necessary on-page optimization techniques. The algorithms are capable of understanding page optimization factors and keyword data. It helps in the automatic analysis of potential keywords and selecting ones that help improve search engine rankings. 

See also  The Best Laptop for Ableton Live In 2020

Search engine giants like Google have developed machine learning algorithms for the optimization of their products and services. It helps in achieving more accuracy through the prediction of user behavior.  However, the application of machine learning to search algorithms makes them a lot more complex. 


Manual identification of ranking potential

For manually processing over 3000 keywords with high ranking potential metrics like CPC and search volume are considered. This helps in understanding the competitive situation for each of the keywords. Higher search volumes indicate competitive keywords, which makes it difficult to attend higher rankings. 

It also allows analyzing competitor web pages. When a competitor page ranks among the top 10 sites for a specific keyword, using the same keyword will also help achieve higher rankings. 

The manual analysis also gives an insight into page optimization factors. If the page has sufficient optimization, obtaining higher rankings can be difficult. Similarly, if there is no target keyword density or the page is not properly optimized, then the on-page optimization measures can help improve rankings. The same set of data can also be used for training the machine learning algorithm.


Collection and analysis of data from different resources

The next most important step is data collection for analyzing trends and patterns. 

Web page optimization tools help in shortlisting keywords and other information based on page ranking. After gathering information, the ranking constraints like URL path, domain name, content title are taken into consideration. This information is saved as numbers so that the machine learning algorithm can process it. If no target keyword is observed from the data is saved with value 0.

See also  Top Artificial Intelligence (AI) trends in 2021

All keywords and domains are represented with numbers. This helps in feeding the input to the machine learning algorithm. The information helps in creating a list of page ranking and keywords based on the competitive market.


Training the machine learning model

In the next stage, the data is split into training data and test data. Training data is necessary for training the machine learning algorithm. Test data is necessary for testing results given by the algorithm. Depending on the different test results, the algorithm is generated. 


Rank prediction for landing page optimization

The model helps in predicting optimization measures for the landing page. Data obtained in the process helps in getting optimization values for the page. The trained algorithm can now predict rankings of the page based on the available information.

Here a machine learning algorithm helps in proving optimization measures for a web page. It only makes use of some basic data and factors like your placement, content title, and title tags to analyze search engine rankings. Leading Machine learning institute in Delhi will help aspirants stand out in the crowd by building their careers in machine learning, thus increasing their chances of working in a reputed organization.

Shabbir Ahmad

Shabbir Ahmad is a freelance enthusiastic blogger & SEO expert. He is the founder of Shifted Magazine & Shifted News. He contributes to many authority blogs including porch, hackernoon & techcrunch.