Machine learning algorithms are different from traditional algorithms in several ways:
Traditional Algorithms
- A person (programmer) creates the program and manually formulates or codes rules.
- The program follows a set of instructions to produce the output.
- The rules are explicitly programmed for each task.
Machine Learning Algorithms
- The algorithm automatically formulates the rules from the data.
- The input data and output are fed to an algorithm to create a program.
- The algorithm learns from the data and improves the learning process of computers based on their experiences without being actually programmed.
- The process starts with feeding good quality data and then training the machines (computers) by building machine learning models using the data and different algorithms.
- The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate.
- Machine learning is an automated process that yields powerful insights that can be used to predict future outcomes.
Another difference between machine learning and traditional algorithms is that deep learning, a subset of machine learning, requires high-end machines. Traditional machine learning algorithms are still machine-like and need a lot of domain expertise and human intervention.
In summary, machine learning algorithms differ from traditional algorithms in that they are automated, learn from data, and automatically formulate rules from the data.
