The potentials of machine learning are proven as many companies around the world are making use of the technology. This technology powers the computer’s ability to “learn” based on real-world data inputs instead of explicit programming.
Machine learning is a branch of artificial intelligence (AI). It focuses on the development of computer programs that can access data and use it to learn. The main aim of artificial intelligence is to allow computers to learn automatically with the intervention of humans or any assistance. The machine learning keeps on growing day by day.
What machine learning can do?
As machine learning products continue to target companies, they are diverging into two channels: those that are focusing on becoming more granular by trying to solve specific issues facing specific verticals; and those that are using machine learning itself to improve machine learning predictability capacity. Although there are a lot of machine learning products across both channels to move data science in the business environment, experts warn that machine learning can’t solve some unique problems for the particular business use case and clean the data in the first place so that it is valuable in the machine learning workflow.
Machine learning can be used to model data
To model data, it requires data scientists to iterate several data models and run them against historical datasets to identify the most accurate predictive models.
Products like DataRobot that are aimed at automating data modeling and predicting processes, while also analyzing when it is best to run big data machine learning activities. As a user of DataRobot, you set your optimal settings and the all the interactive data modeling is done by DataRobot until a set of processed data models emerges with the most consistent accuracy. Then you have the option to deploy top models. This auto modeling and prediction features were tested for business cases that included business analytics, healthcare, insurance, sports analytics, and Fintech. Another example of a company that uses machine learning to model data is Code Pilot.
Code Pilot: uses machine learning to help coders and companies to find dream jobs and talents respectively. The Code Pilot platform reads through an individual’s code, and allow it to stand out by providing key insights to hiring teams and enabling them to make assessments about a candidate’s fit and potential
Context can be tackled by machine learning
A lot of new machine learning markets entrants are focusing on the speeding up of processes around mapping the context that will be needed by a machine learning algorithm to understand and predict need in a given business situation. For instance, if a voice translation learning products was listening in to a customer’s services call in order to more quickly aid the call operator fish out the right solution, the machine learning will, first of all, create an ontology that will comprehend the customer call context like specific language, brand items, and all the vocabulary in that niche.
The people developing these products recognized the need for accuracy. The latest generation of machine learning tools is aimed at speeding up the processes in machine learning and predictive analytics pathway.
Machine learning can be used to predict problems
Machine learning can be used to solve problems like load time for web applications, cloud-based analytic services and more. It can be used to identify potential outliers from web engagement metrics to take care of potential future problems. Some machine learning products with predictive analytics and detection features can identify pattern anomalies in large sets of unstructured data from both machine logs and the behavior of users on websites and mobile applications.
How can we apply machine learning in day to day life?
In video surveillance
It is challenging for a person to monitor several video cameras at once. Computers have been trained to do this. Video surveillance systems are powered by artificial intelligence that makes it possible to detect lots of happenings at the moment they are happening. These systems can give alerts to call for attention whenever something unusual is happening, helping to avoid mishaps.
Machine learning can be used in social media services
It can be used to personalize your news feed and make better ads target. Social media platforms use machine learning for the both their benefit and that of users. For instance, when you upload a picture of you and someone, Facebook will instantly identify the person. They further propose people you may know to you and you see the people are people you know. That is the work of machine learning. It is used to detect false information that circulates on social media. An example of a company that works to identify fraud and other social media related issues are New Knowledge.
New knowledge: This startup was founded in 2015 and uses machine learning to stop the virtual spread of misinformation. New Knowledge builds technology that monitors social media spaces, news outlets and many more things to detect false narratives and stop them in their tracks. It’s more than social listening; it’s social intelligence.
Machine learning tracks your activities to know the type of things you like and recommend them to you. For instance, you did some online shopping some few days ago, and some days after you begin to receive emails on the products that might interest you based on what you shopped online.
Number of startups as well as established businesses have embraced this technology and are making a difference now. There are still lots of ways machine learning can help us, and we can’t exhaust all of that soon. Machine learning has made life so easy for every user like you and me.
Asif Razzaq is the CEO of Marktechpost, LLC. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over a million monthly views, illustrating its popularity among audiences.