Here’s the breakdown of how machine learning (ML) differs from traditional programming:
Approach:

• Traditional Programming: Involves writing explicit instructions and rules for the computer to follow. Programmers define every step and outcome.
• Machine Learning: Focuses on training algorithms to learn from data. The algorithm identifies patterns and relationships in the data to make predictions or decisions.

Data Dependence:

• Traditional Programming: Relies on well-structured and defined data sets.
• Machine Learning: Can handle complex and unstructured data sets, like images, text, or sensor readings.

Flexibility and Adaptability:

• Traditional Programming: The program performs exactly as instructed and cannot adapt to new situations without being reprogrammed.
• Machine Learning: The model can improve its performance over time as it’s exposed to more data.

Common Machine Learning Algorithms:
Here are a few common algorithms used in machine learning:

• Supervised Learning: Algorithms are trained on labeled data sets where the desired outcome is already known. Examples include:
  • Linear Regression: Predicts a continuous value based on a linear relationship between variables. (e.g., predicting house prices based on size and location)
  • Logistic Regression: Classifies data points into two categories (e.g., spam vs. not spam email)
• Unsupervised Learning: Discovers hidden patterns in unlabeled data. Examples include:
  • K-Means Clustering: Groups similar data points together. (e.g., segmenting customers based on buying habits)

These are just a few examples, and the field of machine learning encompasses a wide range of techniques and algorithms.