Introduction to Machine learning

1. Supervised learning (supervised learning):

Given the set of input samples, the machine can push the possible results of the specified target from it.
Two types of target variables are generally used: nominal and numerical.
-Nominal type: The result of the nominal target variable is only in the limited target set value, such as true and false, animal classification set
{Reptiles, fishes, mammals, amphibians}
-Numeric: A numeric target variable can take values in an infinite set of values, such as 0.001,
42.001, 1000.743 and so on. Numerical target variables are mainly used for regression analysis.

2. Unsupervised learning: Data does not have category information and is not given a target value.
-Clustering: Unsupervised learning, the process of dividing a collection of data into multiple classes consisting of similar objects
-Density estimation: The process of finding statistical values of data is called density estimation

Machine Learning Basics
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1. Concept: What is machine learning, converting unordered data into useful information
2. Data acquisition: For example, can be installed in people's mobile phone app, through a number of mobile phone magnetometer to get information
3. Terminology:
-Expert system
-Properties/Features
-Classification
-Target variable (category)
-Training data and test data

4. Task:
1. Supervised learning (knowing what to predict, i.e. the categorical information of the target variable)
-Classification: Dividing instance data into appropriate categories, such as data fitting curves
-regression: Mainly used for predicting numerical data

2. Unsupervised learning (no data category information, no target value)
-Clustering: A collection of data that is grouped into similar objects
-Density estimation: The process of finding statistical values of data
-Unsupervised learning can also reduce the dimensions of data features so that we can display data using two-dimensional or three-dimensional graphs

5. Development steps
1. Collecting data
2. Prepare input data
3. Analyze input data
4. Training algorithms
5. Test algorithm
6. Using algorithms

Introduction to Machine learning