Machine learning, which is basically a category of the algorithm, enables a computer to learn and predict an output from the vast amount of data without being explicitly programmed to do so. It uses statistical learning which requires exploring through data to look out for patterns initiating adjustment of program actions accordingly.
Machine Learning methods
- A skilled Data Scientist is required to provide the desired input and output in a supervised algorithm
- The Data Scientist or rather Data Analyst is to furnish feedback on the prediction accuracy during algorithm training
- Data scientist need to determine the relevant variables or features to be analyzed by the model to develop predictions.
- Such algorithms do not need to be trained
- A deep learning approach is used to review the data and arrive at the conclusion
- Complex processing tasks like image recognition, speech to text and natural language generation are carried out with un-supervised algorithms
- These algorithms require a huge amount of training data and have thus become feasible in this age of big data.
Enterprise Machine Learning
Some of the most common use cases of enterprise machine learning have been explored which are as follows:
Also known as Intelligence Process Automation or simply IPA, it involves the varied use of machine learning. Through natural language processing and deep learning technology, machines can boost the usual rule-based automation system to do better. Business benefits are much more widespread than cost-saving. Machine learning in the enterprise frees up an individual to focus on product modernization and service improvement thus enabling the company to achieve unmatched levels of competence and quality.
Sales of an organization typically generate a huge amount of unstructured data that can preferably be used to train machine learning algorithms. This is really good for enterprises with a large volume of saved consumer data. In order to achieve strategic goals, enterprises are applying machine language to both the sales and marketing sector. By adopting machine learning, enterprises can rapidly develop and personalize content to meet the ever-changing requirements of the potential customer. Machine language models are also beneficial in case of customer sentiment analysis and predictions and analysis of sales forecasting which ultimately helps the sales manager to stay alert in advance.
Due to interactions among high volume consumers, the large quantity of data recorded and analyzed is the ideal teaching material required to train machine language algorithms. Artificial intelligence agents like chatbots and virtual digital assistants are now able to recognize a customer query by simulating interactive human conversation and suggest the appropriate solution swiftly. The efficiency and swiftness of decisions get improved as human agents are freed up to focus more on complex issues and creativity. The time saved due to chatbots and virtual digital assistants taking over the world of customer service is noteworthy.
Security and Fraud Detection
Machine learning can help enterprises to improve threat analysis and ways to respond to attacks and security incidents. While predictive analytics helps to detect infections and threats in an early stage, behavioral analytics guarantees that any incongruities within the system do not go overlooked.
Merging of both human intelligence and machine language leads to developed output for teams that collaborate from different locations. More and more enterprises are planning to use artificial intelligence in their integrated communications and collaboration applications. Metadata indexing for enhanced search gets improved when image intelligence is coupled with object detection. Communication and collaboration between global workgroups in their native language gets facilitated.
Types of Enterprises using ML
Faster delivery and more accurate results in processing large volumes of information have induced a large number of enterprises to adopt machine learning, combining it with artificial intelligence and cognitive intelligence. Below are some types of companies that have taken up machine language as their use case:
- Social Media websites use it to deliver personalized experiences
- Medical and health care industries are able to better diagnose different types of diseases
- Online retail companies are using it for real-time, personalized price variations
- Financial trading companies are able to accurately predict the ups and downs of stock positions and then execute trades based on parameters set by the company with help of Machine learning
- The Marketing industries are using machine learning to identify consumers on grounds of their likes and dislikes, buying patterns or media consumption patterns.
- Data security firms are turning to ML to identify cyber threats early to better defend data.
So this is just the tip of an iceberg. The use case for machine learning has benefited enterprises to a large extent and it assures an increase in significant potential benefits in future. The collaboration of the use cases with enterprise learning will take the business to a whole new level. The companies can enhance their modules and go to a whole new level with the involvement of such cases. It is the new era of the work allowing us to go to the top.