Machine Learning Definition
“Machine learning is the science of getting computers to act without being explicitly programmed”.
You will find this definition of machine learning in various books and over the internet. But as a layman or beginner in machine learning field, it’s very hard to grasp this definition easily. If we don’t comprehend the basic purpose behind something. Then how can we develop our interest to learn different aspects about that?
We know that humans learn from their past experiences and machine follows instructions given by humans. On the basis of past experiences, humans take their future decisions or predict some events will happen or not. Similar is the idea of machine learning. If humans train the machine such that it would be able to collect and analyze the data. Then on the basis of this data collected in the past, machine can predict an event or take its own decision.
Now I believe this article will develop your interest to learn more how machine learning really benefits us we are not aware of.
Following are some real life machine learning examples
1. Machine Learning in Medical Diagnosis
Machine learning in health care has a pivotal role these days. Data collected with the help of machine learning includes symptoms of a disease, medicines recommended before and their results, age, gender etc.
On the basis of this data, physicians can identify :
- a high risk patient
- life of a patient suffering from fatal disease
- recommend optimal medicine
- predict readmission
In addition to this machine learning in medicine industry is also of great importance. A new door has been opened in medicine research. Companies are using data collected from patients and doctors after the use of a medicine. This data helps to upgrade the medicine which brings more effectiveness against the disease.
2. Machine Learning in Marketing Strategy
Machine learning technology has the ability to ingest or fetch the data from unlimited resources. This data can be exploited to constantly review or alter the marketing and sales plan. This data is based on customer reviews and behavioral patterns. Once the data is collected from various sources, machine learning algorithms have the ability to locate precise and relevant data feeds.
Today social media is the biggest source to get data about customer’s interests and needs. People use various social media platforms. Where they discuss about many products. They share their experiences and reviews after using the product. Digital marketing companies have close eyes on all these things. They follow people like a spy on them.
They provide this data to their client’s so they can make a better marketing and sales plan for the future products. They also review their current plan on existing products and come up with a better marketing strategy.
Machine learning algorithms constantly crawl over the thousands and thousands websites to collect data. Google search engine is one the biggest example of machine learning algorithms. It has a huge amount of data of billions of websites. But they also update this data day by day. This data helps google to offer new services. They improve their ads business and search engine results too.
Machine learning is used to find the relationship between two things. This is very important especially for e-commerce website. If a customer buy a product he will be shown similar products or additional accessories related to that product. For example you buy a cell phone from a website. The website will show you phone have similar specifications. It will also show you the accessories you can use with your phone. The new products launched are associated with old products to show better results. this can increase the sales lead.0
3. Machine Learning in Transportation
The Global Positioning System (GPS) in our vehicles is used to monitor location and speed of a vehicles. This data is stored in main server of traffic management system. Machine learning algorithms analyze and draw a statistical pattern of traffic congestion of different areas in different times. This could be helpful to predict areas where traffic jam could be found in a particular time. So traffic management authority can make better plans to avoid such situation.
One of the best example is online cab service like Uber and Careem. When you request a ride on any online transportation service, they use machine learning to predict fare and possible shortest path to your destination.
4. Machine Learning in Social Media Applications
There are plenty of benefits of machine learning in social media platforms. The idea is similar we discussed above machine learns from past activities of users in their feeds, profiles they visit, posts they usually like, comment and share. They even monitor their locations too. After analyzing these statistics, users are shown different advertisements, posts of their interest and other suggestions in their feeds. For example Facebook continuously scans your activities and friends profiles, then it suggests you a list of people you should be connected with.
5. Insurance Industry
Insurance industry is also one of the beneficiaries of machine learning. By using this technology insurance companies design their new policies that customer will purchase. They change and update existing coverage plans. Most importantly they are able to predict any fraudulent insurance claims while designing new insurance plans based of actual and data collected by machine learning.
6. Machine Learning in Speech Recognition
Every mobile device today offer speech recognition service. Apple’s Siri, Microsoft’s Cortana and Google’s assistance are the examples of speech recognition softwares, These softwares basically use machine learning algorithm to convert speech into text.
They are highly intelligent softwares, they can recognize various accents. In addition to this they can reply you appropriate answer of your query with a voice message. Another example is voice typing facility in google documents. Now i don’t need to type my lengthy blogs. I use this service to write my blogs at a very fast pace. I speak, software recognizes and types my blog.
Using machine learning process, speech recognition software predicts what actually the speaker is asking and based on previous data it responds.
7. Machine Learning in Image Processing
This is one of the most used application of machine learning. Machine learning algorithms are used to recognize a person using data from its face. Images of the face are captured then programming algorithms are used to estimate the overall face structure of a particular human. For example the distance between nose, lips, eyes, chin etc is recorded in the data base. This data is used in future to distinguish a person from others usually for surveillance purposes
Machine Learning vs Artificial Intelligence
The idea of Artificial Intelligence (AI) has been under discussion since 1956 when John McCarthy used this term for the first time. But this technology has been attaining notable improvements for the last few years. One of the main reasons is the availability of large amount of data (about 2.5 Quintillion bytes per day) and low cost computational solutions are easy now. Due to this researchers can get results in quick times.
As we have already talked about that we humans learn from our present and past experiences. We have a lot of data in our brain resulted from different events happened in our life. Using that data intelligently we make relationship between present and the past events. After that we make our new decision. In other words we are machines and passing through the learning process day by day. Storing data and implementing that data in the future.
AI is a the area of research where “Intelligent Machines” are studied and designed. The idea is to equip the machine with all aspects of learning and intelligence. In other words creating a machine with the ability to resolve problems we human can do with our natural intelligence.
So we can conclude that AI is an implementation of machine learning. Machines learn from the data collected and intelligently use data to make appropriate decisions an predictions.