In this era of advanced technology, we are surrounded by a significant number of facilities provided by different types of software. One of the latest technologies that are growing rapidly is termed as Machine learning, an important application of Artificial Intelligence. Algorithms of machine learning are used in the applications of email filtering, detection and computer vision that relates to computational statistics, and mainly targets making predictions using computers. Basically, one needs to build a mathematical model of sample data that is termed as “training data” in order to control the decisions as per the situation without using explicit programming to perform a task.
Let’s have some more information about machine learning:
Machine learning is an application of artificial intelligence (AI) that makes the system capable enough to automatically learn and improve from experience without using any type of explicit programming. It mainly targets the development of computer programs that helps in accessing the data which further can be used for learning about the particular scenario.
- Medical predictions and diagnoses:
In the medical industry, machine learning helps a lot by making easy identifications of high-risk patients, showing nearly perfect diagnoses and recommendations of best possible medicines with a prediction of readmissions. These activities predominantly based on the results of available datasets of patient records as well as the symptoms exhibited by them. Machine learning provides the facility for faster patient recovery without any need for extraneous medications. In this way, machine learning makes it possible to deal with patient health at a minimal cost.
- Spam Detection:
One of the most important problems solved by machine learning is the detection of spam. At the starting, for filtering out the spam, rule-based techniques were used by email providers, but with the advent of machine learning (ML), and involvement of a brain-like neural network provides us the facility to eliminate the spam emails. They recognize the phishing messages and also avoid junk mails by evaluating the rules across the huge network of computers.
- Managing Data Entry:
For a particular organization, there are various types of issues that can block the way to success and can be a matter of high priority to deal with. In the same way, data duplication and inaccuracy are some of the major issues that may confront at the time of documentation and other data-related activities. But with the help of predictive modeling and machine learning algorithms, machines can perform time-intensive data entry tasks leaving other skilled resources free that can be used to handle other value-adding responsibilities.
- Product recommendation:
It includes a way to have better sales and marketing strategies with up-selling and cross-selling. Machine learning models analyze the search history, based on that, it shows the right product from the inventory in which a customer is interested. Algorithms of the model identify the hidden patterns among the item and group all the similar products into the clusters, this process of clubbing the similar products is termed as “unsupervised learning”, which is a specific type of machine learning algorithm. Hence, such a model helps in making the best product recommendation based on the customer’s area of interest.
- Time constraints:
Coming to the timing factor, machine learning doesn’t provide immediate predictions as it learns through historical data. The bigger the recorded data (search history), the better it will perform. For example, a recommendation of a particular product in which the customer is interested requires some searching from the customer’s end, then the only machine will be able to understand which product has to be shown as a similar product.
- Problem with verification:
Another limitation is the lack of variability, according to Brynjolfsson and McAfee computers are not good storytellers as machine learning systems know more what they can tell to humans. They can’t always provide reasons for a particular decision or prediction. Hence, human collaboration is still an important aspect to better evaluate the outputs of these systems.
Machine learning is one of the most prioritized applications of artificial intelligence, spreading at a rapid scale for a different type of task, it is playing a vital role in diminishing the efforts and leaving the skilled resource free, so that remaining efforts and rest of the resources can be utilized for other added duties.