We wanted to do an overview of AI Machine Learning similar to the popular interview Chad Jones our CEO did with John Gormley awhile ago which you can listen to below.
What is Machine Learning?
At its most basic level, machine learning is a subset of artificial intelligence (AI) that focuses on giving machines the ability to learn from data without being explicitly programmed. In other words, instead of being told what to do, machines can use data to make decisions and improve their performance over time.
How Does Machine Learning Work?
At a high level, machine learning algorithms work by analyzing large amounts of data and identifying patterns or trends that might not be immediately apparent to humans. Once these patterns are identified, the algorithms can use them to make predictions or decisions about new data.
There are many different types of machine learning algorithms, each of which is suited to different types of data and problems. For example, supervised learning algorithms are trained on labeled data, which means that the algorithm is given examples of both input data and the desired output. The algorithm then uses this data to learn how to map input data to output data. Unsupervised learning algorithms, on the other hand, are not given labeled data, and instead must identify patterns and relationships within the data on their own.
Why is Machine Learning Important?
Machine learning is becoming increasingly important in the modern business landscape for several reasons. First, as businesses collect more data than ever before, machine learning algorithms can help make sense of this data and identify important patterns and trends. This can help businesses make better decisions and optimize their operations.
Additionally, machine learning algorithms can be used to automate many tasks that would otherwise require human input. For example, machine learning algorithms can be used to analyze customer service interactions and identify trends that might indicate a problem that needs to be addressed.
Machine learning models can also be trained on individual user data to personalize recommendations or experiences, resulting in improved customer satisfaction.