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How implement Machine Learning algorithms with Python?
Python is also one of the most used programming languages in the world.
In fact, this language offers essential libraries for making statistics, for processing images or even for data analysis.
Furthermore, Python is characterized by having a simple but feature-rich syntax. Certainly being an interpreted language it has a lower execution speed but many of its modules have been developed in C language.
Recall that Machine Learning is a branch of Artificial Intelligence and studies algorithms that are able to process the input data and then be able to make predictions. Today, these algorithms are very useful if you think about the large amount of data that exists on the web and offer an alternative to traditional algorithms.
The development community around Machine Learning in Python is increasingly large and therefore it is easy to find information or tutorials to get explanations. The modules and libraries are in large quantities and we will see them in detail in the next paragraph.
So we just have to start studying and have fun putting into practice what we have learned!
Machine Learning with Python – What Tools Do You Need?
So what are the tools we need in Pytnon?
First of all, a development environment like Jupyter Notebook, very easy to download and install.
In fact, on the installation page you will find the instructions:
pip install notebook
To execute it, just type:
A module that allows you to do Machine Learning with Python is Numpy.
In fact, thanks to the many properties and methods for scientific calculation, it allows you to easily work with arrays and matrices.
It simply installs like this:
pip install numpy
In Linux systems it is necessary to write:
sudo apt-get install python3-numpy
The installation will not last long.
It is then used in Python scripts simply by writing:
One tool that allows you to easily manage DataFrame and Series is the Pandas library.
To install it simply write:
pip install pandas
It is then called in Python scripts like this:
Matplotlib e Seaborn
As a tool for machine learning in Python, the Matplotlib and Seaborn libraries cannot be missing, which allow you to graphically view the processed data.
Installation takes place by writing on the command line:
pip install matplotlib
and we import it as before using import:
pip install seaborn
To be able to use them then I write:
We then use import as usual:
The scikit-learn library implements Machine in Learning algorithms with Python.
Intuitively, the installation takes place as follows:
pip install scikit-learn
To use it, therefore, we import it:
The tensorflow library implements Machine in Learning and Deep Learning algorithms in Python.
pip install tensorflow
We then import the library to be able to use it for our Python scripts:
We have listed only some of the modules to do Machine Learning in Python, there are others that we will talk about later in the guide.
If you already have some basic Python I suggest you choose the module you don’t know, otherwise start with the basics. Here, then, is the link to the complete tutorial: Python language tutorial index.