What is Machine Learning?
Machine Learning is actually a lot many things. The field is quite vast and is expanding rapidly.
Machine Learning is the subfield of Computer Science that gives computers the ability to learn without being explicitly programmed. (Arthur Samuel,1959)
By means of algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being obviously programmed where to look.
Machine learning uses that data to detect patterns in data and adjust program actions accordingly.
Machine learning algorithms are often categorized as being supervised or unsupervised.
Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.
Let’s understand Application of Machine Learning by an example below.
Traffic patterns at a busy intersection, you can run it through a machine learning algorithm with data about past traffic pattern and, if it has successfully “learned”, it will then do better at predicting future traffic patterns.
What is Importance of Machine Learning?
All of these things mean it’s possible to speedily and inevitably produce models that can analyze bigger, more complex data and deliver quicker, more accurate results — even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities — or avoiding unknown risks.
And these all are industries where Machine Learning is widely used
- Financial services: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities.
- Government: Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights.
- Health care: Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
- Marketing and sales: Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history — and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail.
- Oil and gas: Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast — and still expanding.
- Transportation: Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.
I guess below quote by Thomas H. Davenport Says it all.
” Humans can typically create one or two good models a week; machine learning can create thousands of models a week “
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