FAQ    

 

 

How does machine learning (ML) differ from traditional programming?

Machine learning (ML) and traditional programming represent two distinct methodologies in the development of computer programs. Below are the primary distinctions between the two:
  • Programming approach:
    • Traditional programming: This involves a programmer crafting explicit instructions and rules for the computer to execute a task. All possible scenarios must be anticipated, with specific guidelines for each.
    • Machine learning: Rather than defining explicit rules, a machine learning algorithm discerns patterns and connections within the data. Programmers present the algorithm with input and a target variable, and the algorithm learns to predict or decide based on this information.
  • Learning from data:
    • Traditional programming: A traditional program doesn’t learn or evolve over time; it adheres to the initial rules set by the programmer.
    • Machine learning: An ML algorithm enhances its performance with more data, learning from errors and tweaking its parameters for more accurate future predictions or choices.
  • Adaptability:
    • Traditional programming: Programs are static and require manual updates from the programmer to adjust to new scenarios.
    • Machine learning: ML models can adapt to novel situations, making them more flexible and suited for tasks that were not explicitly programmed.
  • Problem-solving:
    • Traditional programming: It is most effective for well-defined problems that have clear, logical rules, such as arithmetic operations or data sorting.
    • Machine learning: It thrives on complex, less well-defined problems, like image recognition, language processing, and predictive analytics, where rules cannot be clearly stated.
  • Time and resources:
    • Traditional programming: Generally, it consumes fewer computational resources and can be more time-efficient, given the fixed rule set.
    • Machine learning: It typically needs vast data and substantial computational power for training. Once a model is trained, however, it can efficiently process new information.
In essence, while traditional programming revolves around writing precise rules for computers, machine learning is about crafting algorithms that can identify patterns and relations in data, enabling them to make informed predictions or decisions without detailed programming for every potential situation.