What is Machine Learning?
Machine Learning (ML) enables computers to learn from data and make predictions or decisions on their own without the need of explicitly programming. It’s part of the bigger umbrella of Artificial Intelligence (AI) which aims to make machines capable of tasks that would normally require human intelligence.
In simpler terms, Machine Learning helps computers understand data, recognize patterns, and predict future events.
Starting to Learn Machine Learning Where to Begin?
To get started with Machine Learning, we need to understand first some basic statistics. This will guide us analyze data and make sense of number in which we will use one of the popular programming languages Python to carry out these tasks.
What is Data Set?
A data set is a collection of information. It can be simply be a list of numbers or complex such as a detailed table various types of data.
Example of an array (lust of numbers):
[120, 180, 150, 220, 220, 95, 110, 135, 180, 200, 160]
| Product | Category | Price | Rating | In Stock |
| Mango Graham | Dessert | 200 | 4.6 | Yes |
| Adobo | Food | 80 | 4.7 | Yes |
| Leche Flan | Dessert | 120 | 4.8 | No |
| Gulaman | Beverage | 30 | 4.2 | Yes |
| T-Shirt | Clothing | 150 | 4.5 | Yes |
Types of Data
When analyzing data, it is important to understand what type of data we have. The three main types are:
- Numerical data: These are numbers:
- Discrete: Countable numbers such as the number of items in stock.
- Continuous: Measurable numbers, like the price of a product.
- Categorical Data: Data that represents categories like the product’s type or yes/no answers.
- Ordinal Data: Data with a defined order such as ratings (4.8 stars is better than 4.2 stars).
Understanding the type of data helps you choose the correct analysis method.