Machine Learning 101

In recent years the term “machine learning” has been popping up in various discussions and forums, but what exactly does it mean? Machine learning can be defined as a method for data analysis, based on pattern recognition and computational learning. It is comprised of different algorithms like neural networks, decision trees, Bayesian networks, etc. Machine learning uses these algorithms to learn from data and recover hidden insights from data. The learning process is iterative, so the new data is also handled without any supervision. The science to learn from previous data and use it for future data is not new, but it is gaining more popularity.

What Is Machine Learning?

While some people believe that machine learning is no better than traditional methods of computer programming that are still in use, many consider machine learning to be a revolution in the field of artificial intelligence (AI). They believe that using this technology, machines will be able to learn things and do things with their own experience, rather than to simply follow human instructions.

To understand more about the meaning of machine learning, we can compare it to traditional computer programming. The following sections will discuss more about the machine learning and its difference from traditional programming. (For some of the pros and cons of machine learning, see The Promises and Pitfalls of Machine Learning.)

What Is Traditional Programming?

When we program a computer, what we are actually doing is giving it directions in a language that it understands. Then, when we give it an input, it gives an output based on the instructions that we have given to it.

Now, let’s imagine that you have given an input to apply for a credit card. While processing your input, the system will look at all the important parts of your application, take the necessary information and process it. After that, it will produce the output of acceptance or rejection based on the program that was fed to it.

How Machine Learning Is Different

If you use machine learning in the place of traditional programming methods in the credit card scenario, then the result would be somewhat different. The result would actually be based on the input data and the system will gain experience by processing that input data. There won’t be any special program for it. As it gains more and more experience, its performance will get better with time.

So, machine learning actually learns by analyzing the large quantity of data files made with each usage of the system. As it analyzes the data, it changes its programming according to newer demands. This leads to improvement in its accuracy as well. We can also say that machine learning is like a linear regression, where the variables and parameters are changed to better match the input provided.

The Most Popular Machine Learning Methods

The most popular methods of machine learning are the unsupervised and supervised learning methods. Among these, the supervised method is most commonly used. About 70 percent is supervised and 10–20 percent is unsupervised. Semi-supervised and reinforcement learning are also used in many cases.

Supervised Learning

In this method, the algorithms are included with labeled examples, where labeled data means that the data is given a description. The machine learning system will receive both inputs and their corresponding outputs. Now, the system can gain more experience by comparing the actual outputs with the correct outputs to find the errors.

After analyzing the outputs and determining the errors, the system will change its programming accordingly. The system can use different methods to predict the label on unlabeled data. This method is used to do future event predictions based on past data.

Semi-Supervised Learning

This kind of machine learning method is used in similar instances, but it also uses unlabeled data while training. Unlabeled data is anything that can be obtained naturally from the world but does not possess any sort of explanation or description. Usually semi-supervised learning works with unlabeled data more than labeled data, but it can use labeled data too. This is because unlabeled data can be gathered easily.

This method also has the same factors of learning, i.e. prediction, classification and regression. This is considered the best method when the cost involved with supervised learning is too high.

Unsupervised Learning

This method is used for unclassified data, i.e. unlabeled data. Thus, the system knows nothing of the correct output and so, the algorithm has to determine the correct output itself. It can do this by finding some structures within the data. This type of machine learning method is perfect for transactional data. The factors of learning here are nearest-neighbor and self-organizing mapping, along with singular value decomposition and k-means clustering.

Reinforcement Learning

This machine learning method is used in places like gaming, vehicle navigation and robotics. In this method, the system learns by a trial and error method. The main goal in this method is to determine the output in the least amount of time, which can be done by following a suitable policy.

The Importance of Machine Learning

The importance of machine learning is growing day by day due to the same reasons that have made the concept of data mining so important. These reasons include cheap and powerful computational power, large amounts of data being created every day and inexpensive and high-capacity data storage methods. These factors ensure that powerful and accurate models can be made very quickly, which can be used in analyzing big data reserves in bulk, in order to get better, highly accurate outputs. This means that better models can be used for making better corporate and business decisions in less time, and without any human effort.

One way to make such accurate models quickly is automated model making. This model should be dynamic enough to keep up with the changing times. While humans can generally create two models a week, machine learning can create thousands of accurate models by analyzing data quickly. That is why machine learning is so important for businesses and other fields.

Machine Learning Algorithms and Processes

Machine learning algorithms help in getting the most out of big data, by helping the system to quickly analyze the data and produce accurate results. These algorithms help in creating a model which can be used for making business decisions. Some algorithm types are neural networks, random forests, k-means clustering, self-organizing maps and nearest-neighbor mapping.

However, using algorithms is not all. The best model can only be made by following a suitable process. Some examples of such processes are comprehensive data management, interactive exploration of data and visualization of the results found by the model.

The Impact of Machine Learning on Business

The impact of machine learning on business is tremendous. Machine learning has opened new possibilities for businesses. It has helped in accurate model making, which has in turn helped in making better and smarter decisions quickly.

Some Practical Use Cases

Machine learning is being used in many places. Recommendation systems use machine learning to make recommendations to users in real time based on their preferences and current market patterns.

Promedas, which is medical software or a “desktop doctor,” is a machine-learning-based program that uses hundreds of years of medical knowledge to help the doctors in detecting and diagnosing disease. It makes use of the data collected from previous records of disease patterns to identify disease in patients. This helps in more quickly identifying and treating ailments.

Amazon has automated its employee access granting and revocation through a computer algorithm which can predict the resource access for each employee. For this, the computer algorithm uses the records to analyze the risks and how trustworthy an employee happens to be in general.

Machine learning algorithms are also being used by Cornell University to detect whales in the ocean through sound recordings so that ships can avoid the whales. The algorithm analyzes the migration data of whales (where they go and when) to determine the path a ship should take in order to avoid the whales. (To learn more about practical uses of machine learning, see Machine Learning & Hadoop in Next-Generation Fraud Detection.)

Conclusion

As data is growing day by day, the importance of effective processing is also growing. For this, machine learning methods have been devised, which helps the system in making smart decisions, with minimal involvement of humans. The present impact of this technology on the IT sector has been tremendous, so it is easy to imagine that its importance will only continue to grow in the future.