What is Machine Learning? A Simple Guide to How It Works

Machine Learning (ML) is a branch of Artificial Intelligence that allows computers to learn from data without being explicitly programmed.
It enables systems to improve performance on tasks through experience, just like humans learn from practice.
Instead of following fixed rules, ML systems analyze patterns in data to make predictions or decisions.

Types of Machine Learning

Supervised Learning: The model is trained on labeled data, where each input has a known output.
- Example: Predicting house prices based on historical data.
Unsupervised Learning: The model identifies patterns and relationships in data without labeled outputs.
- Example: Grouping customers with similar behavior (clustering).
Reinforcement Learning: The model learns by interacting with an environment and receiving rewards or penalties.
- Example: Training a robot to walk.

Step 1: Data Collection
- Gather large and relevant datasets to train the model.
Step 2: Data Preparation
- Clean the data, remove duplicates, handle missing values, and organize into a usable format.
Step 3: Model Selection
- Choose the appropriate algorithm (e.g., decision tree, neural network).
Step 4: Training
- Feed the data into the algorithm to find patterns and make predictions.
Step 5: Evaluation
- Test the model’s accuracy using new data it hasn’t seen before.
Step 6: Deployment
- Use the trained model in real-world applications like recommendation systems or spam filters.