It seems every other week, the world manifests a new mania, something to dangerously obsess over. The new mania that has currently enveloped the world has proven to be more than just a trend, unlike its countless predecessors; It is an era. The era of Artificial Intelligence is upon us, and it is safe to say, there is no looking back from here.
Using artificial intelligence, machines can perform almost any intelligent task, from painting a rare aesthetic to deciding on massive business decisions, giving a whole new meaning to the term “smart technology”.
And underneath the infamous term, Artificial Intelligence, is a relatively less glamorous word- Machine Learning. Machine Learning is the foundation of AI, and the focus of this article.
Machine Learning is a way of programming a computer to learn to do a task on its own, rather than having you implicitly tell it how to accomplish the task.
Computers do everything they are told to. But, you have to instruct them every single step of the way, failure to do so will result in errors. It is an exhausting task, especially for programmers, who are the ones tasked with ‘speaking’ to the computers. Machine learning is a way of telling the computer “think and do” and having it perform just that, requiring little input from human beings.
Machine learning has descriptive uses where the system uses data to explain what happened, predictive uses where it will use data to predict what will happen and prescriptive uses where the system will make suggestions about what actions to take.
There are some terms used in the machine learning field that would be useful to know, in order for you to properly grasp the article as we delve further into this topic.
A feature is a individual measurable property of data used as input for the algorithm. Think of features as the characteristics or traits. For example, if you’re analyzing images of fruits, features could be color, size, and shape. Features are also called inputs, attributes, predictor, regressor, covariate, explanatory variable, controlled variable and independent variable.
A label is the output or target value in supervised learning, the known answer that will be provided with the input data to train the algorithm. A label is the correct answer or outcome you want the computer to learn. For instance, in an image of a cat, the label would be “cat”. Labels are also called outputs, response, regressand, explained variable, predicted variable, or dependent variable.
Training data is the labeled dataset used to teach a machine learning algorithm. It consists of input features and corresponding output labels used for learning. Training data can be seen as a bunch of examples where you know the answers. The computer studies these examples to understand how things are related.
A machine learning model is the learned representation of the data after training the algorithm. It is the mathematical representation of the relationship between input features and output labels. A model is like a set of rules or patterns the computer learns from the training data to make predictions. It is a program that should be able to find patterns or make decisions from a previously unseen dataset.
An algorithm is a set of mathematical and computational instructions that a machine learning model follows to learn patterns from data and make predictions. Algorithms are step-by-step instructions that the computer follows to learn from the data and make predictions.
Prediction is the outcome produced by the machine learning model when given new, unseen input data. It’s the algorithm’s best guess for the corresponding output value.
Dimension is the number of input variables or features for a dataset. Imagine you have a dataset of houses for sale, and each house is described by various features like the number of bedrooms, bathrooms, square footage, location, and price. Each feature represents one dimension in the dataset. And a house is a five-dimensional point, unique in that each is a combination of all features which differ. The number of dimensions in a dataset is essential because it directly affects the complexity of the problem and the computational resources required to analyze the data.
There are mainly four types of machine learning: Supervised machine learning, semi supervised machine learning, unsupervised machine learning and reinforcement machine learning.
Supervised machine learning
In supervised machine learning, the input and output for our machine learning model are both available to us. That is, we know what the output is going to look like by simply looking at the dataset.
The machine learning model is supposed to choose the outcome from a known set of possible outcomes. This set of possible outcomes is formed by the set of labels present in the data. The model tries to learn the relationship between the input features and the output label during its training. Supervised machine learning is of two types: Classification and Regression
Classification is a method where the model tries to predict the correct label of a given input data. The computer will be given labeled features and classes (tags), it will ‘learn’ the characteristics that the tags have and try to predict which classes the unlabeled data belongs to, according to the features it possesses. Classification has a categorical output, and the numbers of output values are finite. That is they are countable. Example, ‘True’ and ‘False’ (2 labels) or ‘Amphibian, ‘Reptile’, ‘Bird’, ‘Insect’ and ‘Mammal’ (5 labels) etc.
An implementation of classification is trying to predict which artist sang particular song.
Regression is where there are no clear labels or classes but continuous numeric values. So, in this case we have a continuous output variable which would be a numeric value that depends directly on the features which are present. Regression differs from classification in that the output is a numerical value. For example, people use regression techniques to predict product sales based on the historical data of past sales .
Unsupervised machine learning
In unsupervised machine learning, you don’t know which factors are the important ones, but you have a lot of data. The data is given to the computer, and the computer identifies which factors are correlated with a desired outcome. You don’t have any labels; you just have a pile of data within which there could be a pattern that could be discovered.
The way unsupervised machine learning works is that it goes through the data and finds patterns amongst the data points, as there are no target variables that those data points can be classified into. There are several closeness and similarity metrics which are calculated so that the data points which are similar get grouped or clustered together, and the ones which are not are sent to different clusters.
For problems where patterns are constantly changing, are relatively unknown or maybe do not have enough labelled data, unsupervised learning algorithms are recommended.
An implementation of unsupervised machine learning algorithms is in anomaly and fraud detection, because the unsupervised learning methods are better than supervised learning methods at finding new patterns in unseen future data, and therefore is better at handling events which might be fraudulent.
There are different types of unsupervised machine learning algorithms like clustering, association, and dimensionality reduction.
Clustering is a technique which groups unlabeled data based on their similarities or differences. Clustering algorithms are used to process raw, unclassified data objects into groups represented by structures or patterns in the information. It works by grouping together data into several clusters depending on pre-defined functions of similarity and closeness. Similar items or data records are clustered together in one cluster while the records which have different properties are put in separate clusters.
An association algorithm is a rule-based method for finding relationships between variables in a given dataset. These methods are frequently used for market basket analysis, allowing companies to better understand relationships between different products. Understanding consumption habits of customers enables businesses to develop better cross-selling strategies and recommendation engines.
Dimension reductionality refers to techniques that reduce the number of input variables in a dataset. Datasets with a lot of dimensions can be more challenging to work with, so they may require specialized techniques like dimensionality reduction to simplify the data and make it more manageable for machine learning algorithms. The basic aim of these algorithms is to reduce the number of features to bring it down to the most important and relevant features.
Semi supervised machine learning
Semi supervised machine learning is halfway between supervised and unsupervised learning.
In addition to unlabeled data, the algorithm is provided with some supervision information but not necessarily for all examples. The working principle of semi supervised machine learning is quite simple. Instead of adding labels to the entire dataset, you go through and hand-label just a small part of the data and use it to train a model, which then is applied to the ocean of unlabeled data.
When you don’t have enough labeled data to produce an accurate model and you don’t have the ability or resources to get more data, you can use semi-supervised techniques to increase the size of your training data. For example, imagine you are developing a model intended to detect fraud for a large bank. Some fraud you know about, but other instances of fraud are slipping by without your knowledge. You can label the dataset with the fraud instances you’re aware of, but the rest of your data will remain unlabeled.
Reinforcement learning is a subset of machine learning that allows the system to learn through trial and error using feedback from its actions. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Human involvement is limited to changing the environment and tweaking the system of rewards and penalties. As the computer maximizes the reward, it is prone to seeking unexpected ways of doing it.
Human involvement is focused on preventing it from exploiting the system and motivating the machine to perform the task in the way expected. Reinforcement learning is useful when there is no “proper way” to perform a task, yet there are rules the model has to follow to perform its duties correctly.
There are three types of reinforcement learning implementations:
Policy-based reinforcement learning uses a policy or deterministic strategy that maximizes cumulative reward
Value-based reinforcement learning tries to maximize an arbitrary value function
Model-based reinforcement learning creates a virtual model for a certain environment and the agent learns to perform within those constraint.
Machine learning has developed thanks to certain breakthroughs in the AI field.
The first breakthrough involved realizing that it was more efficient to teach computers how to learn than to teach them how to perform every possible task and give them the information needed to complete those tasks.
The second major breakthrough was the invention of the internet. This led to a massive potential for information storage that had never been seen before. Machines could now look at amounts of data that they’d never been able to access before due to storage limitations. In fact, the amount of data being created is too much for humans to process.
These two breakthroughs made it clear that instead of teaching machines to do things, a better goal was to design them to “think” for themselves and then allow them access to the mass of data available online so they could learn.
A lot more industry sectors have accepted the necessity of machine learning. The amount of data being gathered every day is numerous, and machine learning is best suited to situations with large amounts of data. Machine learning has become the core of some companies’ business models, like in the case of Netflix’s suggestions algorithm or Google’s search engine.
At present, almost every common domain is powered by machine learning applications. To name a few of such industries — healthcare, search engine, digital marketing, and education are the major beneficiaries.
The field of machine learning is experiencing exponential development in today’s time period, especially in the subject of computer vision. Today, the error rate in humans is only 3% in computer vision. This implies that computers are already better at recognizing and analyzing images than humans. The future of machine learning will be the large-scale automation of the field. The current tools that are used today will be considered archaic, and you won’t need to use much programming. There are automated tools to create machine learning models and these technologies are advancing rapidly. The entire end-to-end process will be seamless, and this will increase the amount of people that can do machine learning, not just data scientists.
Machine learning is ready to change our lives in manners that were impossible just decades prior. Over the next few years, machine learning could enable search engines boost both the user experiences and the host experiences rapidly in fast progress. With further *neural network growth and development blended with evolving machine learning techniques, the future search engines will be far better in providing responses and perceptions that are significantly germane to the searchers, explorers of the web.
Corporations could fine-tune their understanding of their target audience using machine learning to inform the enhancement of the existing products, new product development, merchandising, and gross revenue. Developers, programmers, and engineers could customize products far more precisely than ever before with algorithms to break down exactly how their products are used, maximizing value for both the organization and the clients. With more advancements and discoveries in the dynamic field of machine learning and its algorithms, for the clients on a larger scale, we shall start to see exact targeting and fine-tuned customization in the near future.
In the coming decades, machine learning will also be one of the cornerstone methods for creating, sustaining, and developing digital applications.
Machine learning has made a major impact on how we live our daily lives and has done so in a relatively short period of time. Its field is on the high and is growing at a tremendous rate.
Organizations and individuals can now manage huge amounts of data intelligently with a number of different tools available. Some tools are complex and some are easy. The bottom line that remains is that data can be used to work for us and for the betterment of the organization.
Making machines learn have a number of benefits:
Lower labor cost
Helpful for the organization in the long run as it leads to better turnaround time.
No human intervention in the process of generating the output.
The above advantages make it very beneficial for organizations to use machine learning techniques and implement them in order to generate some amazing insights from the data they have.
As new technologies continue to unfold, machine learning algorithms can be used more productively. Although it is highly dependent on the quality of data and not without its faults, Machine learning is a huge part of the future of technology.
*This is only a general overview of machine learning. It is much more broad and deeper. I recommend the book, “Machine Learning – A first course for Engineers and Scientists” for people interested in beginning their machine learning journey.