Machine learning can make predictions and forecasts that are useful to businesses. For example, it can predict how many customers will buy a particular product. It can also help detect fraud by identifying patterns in financial data.
It can be used in email filters to recognize spam and in search engines to rank pages higher based on user behavior. It is also being used in chatbots and for natural language processing.
What is Machine Learning?
Artificial intelligence (AI) has an area called machine learning that enables computers to learn without being explicitly taught. It’s used by self-driving cars, medical diagnostic systems, e-commerce and retail sites, cyber security, and many more of today’s critical business functions.
Unlike traditional data analysis, which relies on averages and summaries, machine learning algorithms can easily analyze massive volumes of data. They can also provide more accurate outputs, which takes the guesswork out of crucial decisions.
There are two main types of machine learning: supervised and unsupervised. In supervised learning, data scientists supply the algorithm with labeled training sets and define the variables that they want the algorithm to assess for correlations. Once the algorithm is trained, it can make predictions or suggestions for new data sets.
On the other hand, unsupervised learning employs algorithms to comb through enormous data sets in search of patterns or trends. Unsupervised learning algorithms may have no predetermined assumptions about the data or what they should be looking for, which can cause them to produce inaccurate results. This type of machine learning is most commonly used for data analytics and anomaly detection, such as identifying fraudulent credit card transactions or login attempts.
The Basics of Machine Learning
Machine learning is one of the facets of artificial intelligence, which is the concept that computers can act in ways that mimic human behavior and “think.” Machine learning algorithms aim to autonomously identify patterns in data sets and apply those patterns in the future.
Ultimately, these algorithms help to predict future trends based on what the machine has observed and learned from past experiences. Machine learning is used in various industries and applications, including self-driving cars, natural language processing, and online customer support.
Supervised learning involves giving the algorithm a specific input and expected output. The algorithms then work to find a correlation between those variables. Unsupervised learning works on unlabeled data, scanning through the information and looking for connections and predictions. Reinforcement learning is similar to a video game, where the computer receives feedback on its performance and learns from its mistakes.
The more a machine learning model can be tested and learned from, the better it can make decisions and predictions in the future.
The Algorithms of Machine Learning
Machine learning algorithms uncover innate patterns in data that lead to understanding and improve your decision-making. They are regularly used to make crucial choices in stock trading, energy load forecasts, and medical diagnostics. Media sites use them to filter through countless possibilities and propose songs or movies to you. Businesses use them to identify customer trends and deliver more personalized experiences.
There are many machine learning algorithms, each with features and functionality. No algorithm is a one-size-fits-all solution for any situation, but comparing the various choices can help you determine the most appropriate for your analytics needs.
Supervised machine learning gets its name because you provide the algorithm with a sample of data with labeled outcomes and then train it to determine how to predict those outputs. Another supervised machine learning technique is ensembling, which combines the predictions of multiple weak models to produce more accurate results on a new sample. Algorithms like bagging and boosting are examples of this.
Unsupervised machine learning looks for patterns in datasets that do not have labeled responses, which can be especially useful when you want to explore a large amount of data without having to spend time manually tagging each response. Clustering is a popular unsupervised machine learning algorithm, which can be applied to, for example, cell phone data to help companies decide how best to place their towers to optimize signal reception.
Applications of Machine Learning
Machine learning is changing how organizations do business by allowing them to discover connections that would be difficult or impossible for humans to uncover. It’s behind many technologies we use every day, including voice-operated devices.
For example, ML algorithms are used in the healthcare industry to predict patient disease risk or identify rare cancer symptoms. And in the transportation industry, it helps make delivery routes more efficient and to improve fleet maintenance by analyzing data.
Another critical application of ML is in cybersecurity services, such as detecting patterns that could signal fraud or identify potential security risks before they become problems. It also creates recommendation systems that suggest goods or services to customers based on their previous actions or preferences. And in the energy sector, ML is helping to find new energy sources and optimize oil distribution by analyzing data from sensors at refineries and pipelines.
It might be difficult for CEOs to keep up with the most recent advancements in ML because it is developing so quickly. But to leverage the power of ML, you need more than just a deep understanding of the technology. You must know how to apply the proper techniques and strategies in your business context. SAS offers a range of courses with in-depth instruction and free software access to help you get started on your machine-learning journey.
