Machine learning is a subset of artificial intelligence (AI) and computer science that uses data and algorithms to learn. It duplicates the way that human beings acquire knowledge, learn, and improve the accuracy of tasks.
In this article, we explore this topic to help you better understand how it is used and how it can help your business.
Machine learning (sometimes referred to as "ml")is a computer technology that can learn and improve independently. This self-improving attribute is one of its greatest utilities. Although it is highly similar to artificial intelligence, it's not the same thing.
To clarify, machine learning is a branch of AI that focuses primarily on algorithms to learn the same way humans do. Over time, this allows it to improve its accuracy and efficiency drastically.
High-level machine learning software can be implemented within organizations to help make critical decisions. This is particularly true for large companies, as analyzing vast amounts of data by hand isn't always practical. But machine learning applications can ingest large quantities of data and parse it for use in the real world, adapting its actions as the data flow comes in.
Initially, it may seem like machine learning technology maybe difficult to grasp, as it's a complex technology. However, understanding the way ml functions is relatively straightforward.
First, machine learning software uses algorithms -- a set of rules to be followed in calculations or other problem-solving operations -- to make a classification or a prediction.
In most circumstances, specific customer, product and systems data is input to the system, and then the machine learning application determines patterns with in that data. It can then use error functions to evaluate the predictions that it makes.
In cases when internal company data volume is too low to produce a meaningful ml model, data enrichment can be performed by aggregating known industry data sets or scraping the web to help improve a model's accuracy. If there are no examples available at first, it can still function and produce results. However, it will improve over time and adjust its outputs and actions as new data is accessed and new examples are provided to a system when they become available.
Machine learning is also able to determine whether or not it has reached a specific accuracy threshold. This is achieved by analyzing a data training set and then assessing its performance. If it deems that its performance is insufficient, the machine learning algorithm will repeat its processes. This occurs until the processes have been optimized and are producing the performance levels required.
ML classifies information in three primary ways:
Here is a breakdown of each type of machine learning.
Supervised learning makes use of data labels or datasets to train its algorithms. As more and more information is fed to the machine learning model, it will attempt to optimize how accurate its results are.
By contrast, unsupervised machine learning instead uses algorithms to analyze unlabeled data. This approach is used to detect data groupings or patterns without relying on human intervention.
Finally, semi-supervised learning is a combination of the two. It operates by using a small data set of labeled information and pulling from a larger dataset of unlabeled information as needed. So, it can essentially train itself optimally while using resources to overcome challenges.
One of the most notable use cases is speech recognition. Machine learning uses natural language processing to convert human speech into writing. In the past decade alone, this type of software has continually broken new ground.
ML also has customer service applications. More specifically, machine learning can be used for chatbots. This allows businesses to offer customer service outside of primary business hours. It should come as no surprise that this can go a long way toward increasing customer satisfaction. ML also powers computer vision using in medicine, detecting cancer, for example.
In this use case, machine learning algorithms pull valuable data from media ML can analyze a large group of images and then make classifications.
ML can also predict equipment failure for manufacturing as well as fraud in banking.
If you've ever received a TV show and or movie recommendations on Netflix, then you've been helped by ML.
Consumer technology has been a driving force in ML applications, however, enterprise use cases have been valued at nearly $6 trillion in opportunity, according to aMcKinsey study.
We have also outlined some case studies from our ML consulting practice.
Machine learning is not without its challenges. Many people worry about the impact that the application of ML will have on jobs in the future. It can take over basic decision-making, and as it evolves, this will allow ML applications to become better at handling complex tasks that previously only humans could complete. The advantage to this for business is mundane tasks can be replace by a machine leaving more complex tasks for humans to manage.
Some people worry about how ml might impact privacy. Since machine learning quickly collects and processes information, there can be unintentional discrimination and bias present.Without rules that limit how data can be used, a machine can easily violate privacy rules. So correct protocols must be followed to avoid this kind of pitfall. Working with a consultant like Bitstrapped can help you navigate this business challenge.
When working with the Bitstrapped team, we can help you develop use case scenarios and identify pitfalls to help you optimize a project and produce the outcomes you're looking for in a machine learning application.
Want to learn more about what we have to offer? Feel free to get in touch with us today and see how we can help.
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