Mlia. 1th. Machine Learning Basics

1. The true meaning of machine learning: Using computers to demonstrate the true meaning behind the data. or the conversion of unordered data into useful information.

2. Several expressions commonly used in numeric values: decimal, Binary, enumeration type.

3. How do I choose the right algorithm?

First consider the purpose of using machine learning algorithms. Consider the value of the target variable you want to predict?

If you want to predict the value of the target variable, you can choose to supervise the learning algorithm, otherwise you can choose unsupervised learning algorithm. If the supervised learning algorithm is chosen, the target variable type is determined, if it is discrete type, if/no, 1/2/3, etc., then the classification algorithm can be selected, if the target variable is a continuous type of numerical value, such as 0.0-100.0, 999 to 999, you can choose to use the regression algorithm.

Unsupervised learning algorithms are optional if you do not want to predict the value of the target variable. Further analyze whether you need to divide the data into discrete groups . If this is the only requirement, use a clustering algorithm, and if you also need to estimate how similar the data is to each grouping, you need to use the density estimation algorithm.

4. Learn the machine learning algorithm, you must understand the data instance, each data instance consists of several eigenvalues. Classification is the basic machine learning task.

Mlia. 1th. Machine Learning Basics