Machine Learning has taken the digital world by storm. It is an extremely useful tool that is used to detect patterns in data and search queries. These results are then used for various tasks like predicting business analytics, making targeted decisions, using insights to drive sales, and stopping spam. The possibilities are endless. But what exactly is Machine Learning?
What is Machine Learning?
Machine Learning or ML is the careful study of computer algorithms that improve over time with the continuous use of data. ML is seen as an important part of Artificial Intelligence.
As the name suggests, machines (computers) learn patterns in the ever-increasing digital data generated on various platforms and then give results to enhance user experience.
These machines need large amounts of data and substantial time as to deliver the correct insights. After that they can then perform without the need for human intervention.
Difference between ML and AI
ML is a subset of Artificial intelligence or AI. AI aims to make computers behave like humans, ML is essentially the key that provides patterns based on datasets and algorithms.
Examples of AI are digital assistants such as Google Assistant and Siri which help us accomplish several tasks. On the other hand product recommendations on Amazon are ML which is shows you a trend based on search and buying behaviors.
Deep Learning vs Machine learning
Deep Learning forms layers of data and makes an “artificial neural network” which enables it to adapt to new information quickly and show results accordingly. It basically mimics the human neural network.
Whereas, ML works only on predetermined sets of data and is quite limited to when new concepts are introduced. It requires lesser amounts of data to perform as compared to deep learning.
Machine Learning vs Data Science
ML is a tool that analyzes the data. It makes use of a technique that enables computers to learn from the data. Whereas, data science is a scientific study of interpreting the data, extracting the meaning and useful insights from it.
Now that you have understood some basic differences between Machine learning and some other subsets of AI let us delve deeper into the world of ML.
Examples of ML
A classic example of Machine Learning can be from our day-to-day life which is speech and face recognition software. These are widely used around the world. Power BI, Google search queries, antivirus programs all are examples of ML.
Besides that, it is also utilized in extracting useful information and giving predictive analysis based on it. Many companies also use ML to recognize fraudulent deals, medical diagnoses, etc.
Benefits of Machine Learning
In business
Business intelligence and ML is getting popular in the business sector as it provides opportunities of scalability, expansion and provides insights into latest trends, buying behavior.
Customer retention becomes easier by studying purchasing trends, natural language processing, the businesses can offer the best offers, entice those customers which have abandoned carts, and run promotions to attract new customers easily.
In short, ML has a major role to play in how businesses take their customers and how customers put their trust in the business.
Healthcare
Healthcare is the one of the most unique sectors to be revolutionized by ML. It has not only significantly changed how doctors and patients interact, but also has renewed the various medical research techniques.
Machine Learning widely aids in early disease diagnosis and treatment which help patients get cured faster have a better prognosis and essentially have better quality of life. Apart from that, it also decreases the number of readmissions.
More over ML, helps doctors and researchers to identify high-risk patients, recommend perfect medication, accurately diagnose the ailment and achieve so much more all with the careful study of the data acquired
Cybersecurity
ML has also played a major role in reducing cybercrimes by recognizing fraudulent activities and fake IDs. It makes use of facial and handwriting recognition systems which allows law enforcement agencies to catch the miscreants with relative ease.
Additionally, it can detect unknown or new activity easily through its new generation software which gives the ample chance to secure sensitive information and data against theft.
Furthermore, it can also detect spam or phishing messages quite easily thanks to advanced neural systems at its core.
Agriculture
Machine Learning has transformed the agriculture sector by employing useful insights. It helps famers to select the best seeds, identify best weather conditions to plant crops and even helps with the selection of appropriate pesticides.
The data gathered from the information helps to increase crop yield, reduce harmful elements, and provide tools that allow agriculture to flourish.
Education
Machine Learning allows has a wide-spread use in the education sector. ML enables educators to become more student-centric by employing different techniques such as customized learning, adaptive training, and providing digital assessment etc.
This dynamic approach allows students to learn in a more interactive environment, retain information for a longer period, and enhance their skillset substantially.
Moreover, ML follows the learning path of students and adjusts the curriculum according to the student’s capabilities and actual needs. The feedback from this data helps instructors to cater to the individual needs of every student.
Conclusion
The digital world is a minefield where any wrong step can blow the entire network. Machine Learning can have its disadvantages as it is limited to a large number of predetermined sets of data.
The data should be unbiased and should be run over a long period to determine its accuracy and functionality.
This makes Machine Learning quite dependent on humans and data. Not everyone can deal with ML as it needs appropriate data selection to provide desired results.
However, having said that Machine Learning will undoubtedly be the future of technology. The benefits of ML demonstrate just how lucrative of a strategy it is for various industries to thrive and provide value to the human race.
