Things to know about Machine Learning
In this modern, increasingly competitive business market, reliable insights into customers and emergent trends can mean the difference between success and failure. To meet this need, businesses turn to data analytics. Machine learning (ML) applies advanced AI solutions, using data and algorithms to create data models. A model is a mathematical expression that approximates the relationship between the variables that appear in the data and enables the ability to predict one from the other. A very simple example of a model would be a linear relationship that predicts a person's weight from their gender and their height. Rather than following specific, pre-programmed rules, machine learning mimics the human learning process, improving itself through experience and training.
Using machine learning solutions to build precise models empowers organizations to analyze extremely large, complex data sets, delivering faster, more-accurate results at scale. With machine learning, businesses enjoy detailed insight into opportunities, risks, and customer needs. And while this often means improved returns, the true possibilities of machine learning may be almost limitless.
Business Applications Made Easy and Quick
Machine learning generally follows a specific process, outlined below:
- Gathering data
Reliable data is collected so that it can then be used to inform the predictive model. - Preparing data
Collected data is pulled together, irrelevant details are removed, and any necessary adjustments are made (such as correcting errors, removing duplicate data, etc.). Data is split into two sets: training data, which is most of the dataset and will be used with the machine learning model, and evaluation data that is used to test the effectiveness of the model after it has trained. - Choosing a model
A model is selected. Many different machine learning models exist, and some are better suited to specific use cases than others. - Training
The refined data is used within the chosen model to incrementally improve that model's predictive ability. - Evaluating
After the model has trained on the training data, it is now tested on the evaluation data. By introducing new data into the model, the effectiveness of its predictive abilities may be assessed. - Parameter tuning
After the model has been evaluated, specific test parameters may be fine-tuned to produce better results.
- Predicting
The final value of the model is realized, and it is used in real-world settings to make informed predictions based on available data.
Digital assistants and chatbots
Machine learning may be applied to chatbots and digital assistants, allowing them to evolve and learn from inputs, and working to maintain natural language processing while gathering and storing relevant information.
Recommendations
The use of machine learning for recommendations encompasses everything from streaming services to retail. A machine learning system gathers information about customers over time, draws correlations about the consistent behaviors and patterns, then provides customized recommendations based on the patterns it has ascertained.
Contextual online advertising
Consumers want to see ads that are relevant to them. Machine learning technology helps populate relevant keywords that are trending in content, while also helping marketers take advantage of brand-building content.
Cybersecurity
An essential aspect of AI security, machine learning helps make cybersecurity simpler, less expensive, more effective, and more proactive. Security AIOps and security operations use machine learning to analyze patterns to help predict and prevent attacks, both similar and new, while adapting to changing behavior.