Accelerated by digital transformation and competition, companies in various industries increasingly use machine learning (ML) to optimize their operations. From cash flow forecasts to customer segmentation, ML algorithms have become the go-to method for businesses to solve many problems.
Almost every app or software uses ML today. In 2021 alone, 41% of companies accelerated their rollout of artificial intelligence (AI) due to the pandemic. The numbers will only continue to grow with improving technology, pushing ML to the forefront of battling business challenges.
Yet every challenge is unique, and each requires a specific ML toolset. This blog post will explore the different types of ML algorithms, their use cases, and common ML roadblocks. But before we dive in – what is ML? And is it the same as AI?
While often used interchangeably, especially in the business world, machine learning and artificial intelligence are two different, although related, terms. AI is a broader concept for describing the science of imitating human abilities, and ML is a branch of AI about training machines to learn.
ML uses data and algorithms to mimic the way humans learn, continuously improving in the process. And just like the human brain, ML depends on input to make needed connections and provide results. ML algorithms look for patterns in data and make conclusions. They learn what to do from given examples and apply the gained knowledge to new data sets.
The main goal of ML is to enable computers to learn without human assistance and adjust when needed. Its core value lies in its ability to solve problems at a humanly unimaginable scale and speed.
Machine learning algorithms are classified into three types: supervised, unsupervised, and reinforcement learning. The type of algorithm will depend on the goal and the available training data.
Supervised learning trains machines using labeled data. Labeled data means that you have already defined what the output from the model can be. In this case, humans act as teachers providing input and showing the correct answers.
In other words, input and output space are specified, the machine just needs to map them. It can also compare the obtained output with the correct output and learn from errors.
So in supervised learning, external supervision is needed to train models to be correct.
Examples: can be used for weather forecast, image recognition, and content classification.
In unsupervised learning, unlabeled data is used, meaning that the output is not known when starting. These algorithms go through data looking for patterns, trends, or connections and return results that follow the same pattern, usually as groups or outliers.
No supervision is needed. Unsupervised machine learning models learn, discover patterns in data, and deliver output independently. Such algorithms are instrumental when experts are unsure what to look for in available data and when they are looking to explore it.
Example: can be used for customer segmentation, churn analysis, or anomaly detection.
Reinforcement learning teaches machines to complete processes that include multiple steps and have clearly defined rules. Positive or negative cues, also known as reinforcement signals, are given to the algorithm as it progresses with a task.
These algorithms interact with their environment by acting and encountering errors or rewards, continuously learning in the process. There is no predefined learning variable and no supervision.
Examples: widely used in the gaming industry, for self-driving cars or robotic hands.
Machine learning is widely used in many different industries. It helps companies identify trends in customer behavior, refine operations and develop new, better products while also gaining a competitive edge. Businesses can use ML for:
As technology evolves, so do new threats to data and privacy. Machine learning algorithms can identify and learn about new malware or security vulnerabilities, preventing your business from possible exposure.
ML-powered customer service chatbots or voice assistants are familiar to many. ML algorithms can also analyze customer behavior by evaluating quantitative and qualitative data, such as time spent on a website or common actions. Recommendation engines can be developed to suggest products based on buyers’ past purchases, wish lists, or other data.
Machine learning algorithms can scan through millions of social media posts and updates to gather customer feedback, allowing companies to keep track of their brand perception and take appropriate action.
Banks and fintech companies use ML to automate trading or to provide financial advisory services. ML algorithms can be used for business process automation, sales forecasting, or stock price analysis.
Larger companies have a lot of data collected over the years of running their business. However, this data is often unusable for ML due to different factors:
Problems with data are one of the most common ML application roadblocks. More than a third of companies cite data complexity as the most significant barrier to adopting AI.
Solving these data issues usually is not companies’ core competence, and they often fail to anticipate just how resource-intensive it can get. By our estimates, around 30% of the costs in AI development are directly related to sourcing and processing data, while approx 80% of the AI project time is spent on data-related activities.
It is possible to resolve data issues and achieve desired results within ML with a proper setup. Partnering with an AI automation company like StageZero can cut the time spent on data by 50% and reduce total project costs.
Learn more about how you can ensure a reliable stream of well-organized data for ML training.