In the fast-changing world of Artificial Intelligence (AI), two words tend to be at the center of discussions—Machine Learning (ML) and Deep Learning (DL). Although they are related, they are not identical.
Knowing their differences assists in selecting the proper method for your business requirements.
Machine Learning
Machine Learning is a branch of AI that allows computers to make predictions or decisions based on data without being directly programmed. It comprises algorithms such as decision trees, support vector machines, and random forests.
These models perform reasonably well with structured data and generally need feature engineering—whereby a human picks which inputs are most appropriate for the job.
Deep Learning
Deep Learning, however, is a more sophisticated branch of ML. It utilizes neural networks—particularly deep neural networks with several layers—to process data. This method is better suited to deal with unstructured data such as images, video, audio, and natural language. Deep learning models, unlike typical ML, automatically derive features from raw data, lowering the input level manually.
In other words, if ML is an intelligent calculator, DL is a thinking machine. ML is good with small datasets, whereas DL is good with big data and high computational power.
Both have their own strengths. ML tends to be faster and more interpretable, while DL is more capable for complex tasks such as facial recognition or autonomous driving.
Knowledge of the difference is critical to developing more intelligent AI solutions—and at Wonder Craftz, we're committed to helping you get the right tools on board for your AI adventure.
