Machine Learning(ML) is the process of computer learning from labelled examples. The examples are called Training Data. Based on this training data, computer comes up with rules. These rules are used later to make decisions or predictions for any new data passed into the algorithm.
Basic Machine Learning System Architecture
ML enables computers to teach themselves by identifying patterns and make decision on uncertain data. There are two type of ML methods:
- Supervised – Training data provided for the algorithm to learn
- Unsupervised – No training data provided
Classifiaction Of algorithms
I will discuss about these in detail in a forthcoming blog.
ML is used in the field of artificial intelligence to make decision. ML intersects with other fields like mathematics, physics, statistics etc., Certain example of ML applications are:
- Face Recognition
- Recommendation Systems
- Spam Filtering
- Character Recognition
- Customer Segmentation
- Weather Prediction
Based on what is to be achieved through ML, it is divided into two types:
- Classification – Categorize object into one of the type/category.
example: If the mail is spam or not. - Regression – Predict a real value.
example: What will be the stock price tomorrow?
I will discuss about these in detail in a forthcoming blog.
Over the years, ML has grown to the level of playing games, composing music and imitating other activities by humans! IBM’s Watson is a good example for this. In next blog, I will discuss about Variables in data, using which the ML trains itself.
Tada - Data 