Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without being explicitly programmed. Usually, machine learning algorithms are trained with large sets of data and then used in subsequent tasks or applications. Machine learning has been widely used for classification and prediction tasks for decades, but only recently has it received widespread attention due to the dramatic improvements in computational speed. Alternatively, it could be referred to as a data science field that uses computer systems to do what humans can do like seeing, hearing, and making decisions. Computer systems can be trained to perform tasks such as speech recognition and detecting computer viruses. Facebook, for example, uses machine learning to customize how each member’s feed looks. A member who regularly reads the posts of a particular group will see more of the group’s activity sooner in his or her feed.
What are the Goals of Machine Learning?
Machine Learning focuses on developing and using computer programs that can learn from data in an automatic fashion. Machine learning is about predicting the future based on vast amounts of data collected over time. The name “machine learning” comes from the fact that computers are able to learn independently. Basically, there are four approaches to machine learning: Supervised Learning, Unsupervised Learning, Semi-supervised learning and Reinforcement Learning. Supervised/Unsupervised is a distinction between training models for classification or regression problems. Let’s break it down:
- Reinforcement learning: This is a type of machine learning that deals with the behaviour of agents in an environment where they must make decisions in order to maximize some notion of cumulative reward. It can be applied to specific tasks in which an agent interacts with its environment. A data scientist typically uses reinforcement learning to train a machine to perform an intricate multi-step process that follows clearly defined rules.
- Supervised learning: This is a machine learning approach which means that the algorithm is given training data consisting of input and output pairs in order to build a generalizable function to make predictions. The supervised learning method consists of input-output pairs where the algorithm learns from examples. The goals are for it to learn the relationship between features and outputs as well as create models of how these features affect each other.
- Unsupervised learning: This type of machine learning involves training algorithms off of unlabeled data. Data sets are scanned for meaningful connections using the algorithm. It is predetermined what data algorithms will use to train on and what predictions or recommendations they will publish.
- The semi-supervised method: This can be used for both classification and regression tasks. A combination of the two types of machine learning is used in this approach. A model is free to explore and develop its own understanding of the data set on its own due to a mixture of labelled training data fed to it.
Uses of Machine Learning
- Image Recognition: It has been found that the capability of a computer to recognize an image or a pattern is often limited. Machine learning can be used to identify patterns in images. For instance, certain images may show a specific animal such as a lion or dog and machine learning can be used to detect that pattern even if it is not shown explicitly within the image itself. It would then go on to search for all other pictures that contain that, which will increase your chances of finding what you are looking for and save you time and effort.
- Autonomous Robots: The recent advancements in AI have allowed us to make robots autonomously capable enough to work independently. These robots could be used for hazardous activities such as searching for survivors after a disaster, scouting out the dangerous area before human exploration and resource gathering.
- Real-time translation: Current methods of real-time translation involve converting text from one language into another. However, these are often inaccurate due to limitations in the tools used and the amount of data that can be processed at once. Machine learning is capable enough of identifying patterns within a text and pasting them into new text with ease. This would allow real-time translations between languages with ease.
- Customer relationship management: CRM software can use machine learning models to automatically identify customer behaviour patterns and the associated risk factors. Customer management systems now have the ability to detect a potential customer’s stress levels, buying habits and behavioural patterns.
- Drug discovery: Machine learning can be used to predict if the medicine is likely to have side effects or not prior to administering it to its users allowing for early detection and prevention of further damage or suffering.
- Supply chain optimization: Machine learning can be used on a large scale to manage supply chains by tracking patterns in terms of information that are specific to supply chains such as variation in shipment dates, number of items shipped out etc. This will help companies increase efficiency and reduce costs associated with changing their shipping routes.
Advantages of Machine Learning
As artificial intelligence nears closer to the world of science fiction becoming reality, there is no shortage of information on the topic. That makes it difficult for people to understand which aspects of machine learning will be most useful for them and what implications they can expect. This innovative method of technology is able to capture data for tasks that were previously impossible without someone programming them every step of the way. But because it’s so new, you may be wondering what machine learning is good for and if it will work for you.
Here are some of the advantages of machine learning:
- Machine learning is a form of artificial intelligence that enables computers to learn without being explicitly programmed.
- Machine learning offers new ways of thinking about problems and solving them with software algorithms.
- Machine learning also can be used in the context of systems that work autonomously to make decisions and take actions by themselves, using data as the basis for these decisions or actions.
- Machine learning is transforming diverse fields including medicine, computer vision, linguistics, natural language processing, social networks analysis, robotics and more.
- The machine-learning revolution is producing rapid development in areas such as high-performance computing (HPC).
Disadvantages of Machine Learning
- It can be expensive: Typically, machine learning projects are led by data scientists who are well compensated. Software infrastructure for such projects can be costly as well.
- Another issue is machine learning bias: Relying on data sets that exclude certain populations or have errors can create inaccurate models of the world that are ineffective.
- There is a learning curve and it might be hard to figure out how to use it for your problem, especially if you’re not a data scientist or machine learning expert.
- It takes a lot of data preparation (especially if you want predictions on “sensitive” attributes such as race, gender or political preference).
The Future of Machine Learning
While machine learning algorithms have been around for decades, the past few years have seen a dramatic increase in both the size of the datasets that can be analyzed, as well as in computing power. This is for good reason: As datasets grow larger and more complex, machine learning becomes a necessary tool to extract value from them. It’s also incredibly valuable for companies who are looking to automate processes or replace human labour with automation. Machine learning continues to gain importance to business operations and AI becomes more practical in enterprise settings, leading to a sharpening of platform battles.
Researchers are increasingly working on developing applications that go beyond deep learning and AI. AI models require extensive training to produce highly optimized algorithms for one task. In addition to trying to find ways to make models more flexible, some researchers are also searching for ways to allow the machine to apply the context it learned in the past to tasks in the future.
In this post, we’ve explored the basics of machine learning which you can use to start implementing machine learning algorithms in your next project. We started with the definition of machine learning and how it works by focusing on supervised and unsupervised approaches. We then explored some applications of Machine Learning as well as its pros and cons. Finally, we ended with some predictions for the future. Overall, We hope this post gave you enough to be able to begin using machine learning in your next projects. In the meantime, click here to read our article on artificial intelligence.