Machine learning offers substantial opportunities to categorize and analyze data, something every company wants to do. As a branch of AI, machine learning occurs by applying algorithms to structured and unstructured data to find interrelationships, anomalies, and trends. Its conception was to imitate how humans learn, and these algorithms actually become “smarter” over time, improving accuracy.

The Origins of Machine Learning

The term originated from Arthur Samuel of IBM conducting a research project on checkers. The checkers master Robert Nealey lost to a computer Samuel built way back in 1962. When thinking about the achievements of machine learning nearly a quarter into the 21st century, this seems trivial, but everything has a beginning. In the decades since the computer’s chess victory, machine learning has become more mainstream and critical in big data analysis. 

How Machine Learning Works

The early stages of machine learning involved theories of computers recognizing patterns in data and learning from them. Since that time, the process has become more complex. The basics are the same—computers learn how to think as humans do. Now, its application enables companies to transform processes, assigning tasks that only humans could complete in the past to machines. 

The process corresponds to algorithms, and they have three main parts:

• Decision process: Based on input data, which may be labeled or not, algorithms produce an estimate of a data pattern. 
• Error function: This serves as the means to evaluate the prediction of the model. If known examples exist, an error function can compare the two to assess accuracy.
• Model optimization process: If the model can fit better into the training set's data points, weights can adjust to reduce the discrepancy between the known example and the model estimate. The algorithm repeats this evaluation and optimization process, updating weights autonomously until reaching a threshold of accuracy. 

Machine Learning Methods and Use Cases

So what are the practical applications of machine learning? Can you apply it to any process? In theory, yes—but a large amount of data is necessary for the learning to occur. 

There are three categories of machine learning methods:

• Supervised learning: This method uses labeled datasets to train algorithms to classify data or predict outcomes accurately. It can help organizations solve real-world problems at scale, such as predicting customer lifetime value and understanding consumer behavior. It can also offer recommendations to users (e.g., eCommerce, YouTube).
• Unsupervised learning: This approach works with data that isn’t labeled. Algorithms attempt to discover “hidden” patterns without human intervention. In finding similarities, opportunities can become apparent. Use cases include customer segmentation and fraud detection.
• Semi-supervised learning: The final type of machine learning is a hybrid of the others. It uses a smaller labeled data set to guide classification from a larger unlabeled dataset. Applications include speech analysis and classifying large groups of text documents. 

The Challenges of Machine Learning

Implementing machine learning into your company isn’t as simple as having data. The first challenge is collecting and aggregating data into a single source from legacy systems or other siloes. Overcoming that challenge can be a significant hurdle. When you do, there will likely be more hindrances, including infrastructure requirements, because it requires considerable processing power. 

Companies also avoid adopting machine learning because of the time it takes and the costs involved. If the process is too slow, it won’t be possible to react to the insights derived while they’re still valuable. 

The single biggest obstacle is attaining the right talent. Data scientists are an expensive and in-demand resource. 

When they see all the adoption challenges converge, most companies decide not to use machine learning. A Census Bureau report found that only about 9 percent of U.S. firms employ it. For those that do, around 90 percent of machine learning models never make it into production. 

However, new platforms can alleviate many of these challenges so companies can realize the benefits of machine learning. 

You Don’t Need to Be a Data Scientist to Operationalize Machine Learning  

New technology platforms can do the heavy lifting for you, from gathering and cleaning the data to ingesting and delivering visualizations. Anyone can transform this complex process into actionable insights.