Machine Learning with Python language
Python is a popular programming language to use in machine learning because it offers developers exceptional versatility and power while integrating with other software
Python, when used in machine learning, offers developers of all skill sets exceptional versatility and power. Developers can use Python to develop a variety of applications because it integrates well with other software while its simple syntax makes it a good choice for coding algorithms and collaborating across teams. Python also has a huge number of libraries and frameworks that are very good for machine learning (such as Scikit-Learn), which handle basic machine learning algorithms.
- Machine Learning is making the computer learn from studying data and statistics.
- Machine Learning is a step into the direction of artificial intelligence (AI).
- Machine Learning is a program that analyses data and learns to predict the outcome.
Machine learning need to mathematics and study statistics, and how to calculate important numbers based on data sets. We will also learn how to use various Python modules to get the answers we need. And we will learn how to make functions that are able to predict the outcome based on what we have learned.
Data Set
In the mind of a computer, a data set is any collection of data. It can be anything from an array to a complete database.
Example of an array:
[99,86,87,88,111,86,103,87,94,78,77,85,86]
Data Types
To analyze data, it is important to know what type of data we are dealing with.
We can split the data types into three main categories:
- Numerical
- Categorical
- Ordinal
Numerical data are numbers, and can be split into two numerical categories:
- Discrete Data
– counted data that are limited to integers. Example: The number of cars passing by. - Continuous Data
– measured data that can be any number. Example: The price of an item, or the size of an item
Categorical data are values that cannot be measured up against each other. Example: a color value, or any yes/no values.
Ordinal data are like categorical data, but can be measured up against each other. Example: school grades where A is better than B and so on.
By knowing the data type of your data source, you will be able to know what technique to use when analyzing them.
You will learn more about statistics and analyzing data in the next chapters.
To use Machine learning with Python you need to learn:
- Python language: Python Tutorial
- Matplotlib Tutorial
- Machine Learning with Python
Conclusion
In this post we have talked about Machine Learning with Python. Python is best language to use with Machine Learning because of there is a huge number of libraries and frameworks that are very good for machine learning (such as Scikit-Learn), which handle basic machine learning algorithms.
the three above tutorial is to undrestan Python and how to use python library with Machine learning.
This post is part of “ML.NET-Step by step”.