Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Historical or back data has been primarily used for following two purposes until a few decades ago:
- As a Record to know what happened.
- To identify the root cause of why it happened.
Despite the fact that the reasons mentioned are valid, we have added a dimension in the last decade where data is being utilized for predicting what could potentially happen in the future. Then comes Machine Learning which play significant role in doing so. Machine learning is a subset/subfield of Artificial Intelligence. Generally, the main aim of Machine learning is to understand the structure of data and apply the best possible models that can be utilized or identify hidden pattern. Developing machine learning model is one the key factors in predicting a future problem which again requires machine learning algorithms. There are numerous machine learning algorithms which have been developed and mature enough to solve various real world business problems.
Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs.
Using Machine learning, information is being turned into knowledge. In last 5-6 decades, enormous data has been recorded or collected which will be of no use if we don’t utilize or analyze to find hidden patterns. In order to find useful and significant patterns with the complex data, we have several Machine Learning techniques available to ease our struggle for discovery. Subsequently, those identified hidden patterns and knowledge of the problem can be helpful to performs complex decision making and predict future occurrence.
Machine Leaning Methods
the classification of data on broad scale can be done into two categories, namely Labeled data and Unlabeled Data
There are many approaches that can be taken when conducting Machine Learning. They are usually grouped into the areas listed below. Supervised and Unsupervised are well established approaches and the most commonly used. Semi-supervised and Reinforcement Learning are newer and more complex but have shown impressive results.
According to the famous Machine Learning concept No Free Lunch Theorem, there is no single algorithm that will work for all tasks i.e. each task has it’s own idiosyncrasies.
Let’s explore supervised and unsupervised methods in more details.
Supervised learning is primarily used to address two kinds of problems (Identifying Value): regression and classification problem.
In supervised learning, the goal is to learn the mapping (the rules) between a set of inputs and outputs.
For example, the inputs could be the weather forecast, and the outputs would be the visitors to the beach. The goal in supervised learning would be to learn the mapping that describes the relationship between temperature and number of beach visitors.
In supervised learning, the computer is provided with sample inputs that are labeled with their specific outputs. The motive of this method is for the algorithm to be able to “learn” by comparing its actual output with the “taught” outputs to find errors, and modify the model accordingly. Supervised learning therefore uses patterns to predict label values on additional unlabeled data.
A common use case of supervised learning is to use historical data to predict statistically likely future events. It may use historical stock market information to anticipate upcoming fluctuations, or be employed to filter out spam emails. In supervised learning, tagged photos of dogs can be used as input data to classify untagged photos of dogs.
Unsupervised learning is primarily used to address clustered data. (Identifying data pattern).
In unsupervised learning, only input data is provided in the examples. There are no labelled example outputs to aim for. But it may be surprising to know that it is still possible to find many interesting and complex patterns hidden within data without any labels.
An example of unsupervised learning in real life would be sorting different color coins into separate piles. Nobody taught you how to separate them, but by just looking at their features such as color, you can see which color coins are associated and cluster them into their correct groups.
In unsupervised learning, data is unlabeled, so the learning algorithm is left to find commonalities among its input data. As unlabeled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable.
The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data.
Semi-supervised is a middle road between supervised and unsupervised approaches. There will be mixture of a small amount of labelled data with a much larger unlabeled dataset which reduces the burden of having enough labelled data. Consequently, many problems arise which need solution with Machine Leaning.
The last one, Reinforcement is less common and much more complex, however it has an incredible applications. If you’re familiar with psychology, you’ll have heard of reinforcement learning. If not, you’ll already know the concept from how we learn in everyday life. In this approach, occasional positive and negative feedback is used to reinforce behaviours. Think of it like training a dog, good behaviors are rewarded with a treat and become more common. Bad behaviors are punished and become less common. This reward-motivated behavior is key in reinforcement learning.
Games are very popular in Reinforcement Learning research. They provide ideal data-rich environments. The scores in games are ideal reward signals to train reward-motivated behaviors. Additionally, time can be sped up in a simulated game environment to reduce overall training time. A Reinforcement Learning algorithm just aims to maximize its rewards by playing the game over and over again. If you can frame a problem with a frequent ‘score’ as a reward, it is likely to be suited to Reinforcement Learning.
In next chapter, we explore more about approaches, algorithms and will proceed to real solutions of Machine Learning.
Machine Learning (Artificial Intelligence) is now became a important of our daily life which incredible applications. Here I have introduced Machine Learning and approaches with real world business cases. Keep your eye out for more blogs!!
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