In order to bring closer the ideas behind some interesting and important concepts of working with data and machine learning itself, a number of lessons on the course are accompanied by materials with codes. In this lesson, you will find guidelines on how to use these materials and prepare the environment for work. In the data analytics and machine learning community, the combination of the Python programming language and Jupyter volumes is very common. That is why they will follow you on this course. For data analysis, we will use the Pandas library, a popular library for working with tabular data, and a library for visualizations Matplotlib.Za implement machine learning algorithms, we will use the NumPy library, a specialized library for fast numerical calculations, while we will get to know the work with some interesting programs through the TensorFlow and Transformers libraries. Each of these libraries will be discussed in the future.
All materials that will be used for the course are available in the https://github.com/AIForVet/aiml repository. As we said, these are Jupyter notebooks with fragments of Python code. The number in the volume name corresponds to the ordinal number of the section in which the material is used, while the name corresponds to the name of the lesson.
If you want to execute these materials on a local machine, the easiest way is to install the Anaconda environment. You can find the version of the environment suitable for your operating system on the official download page. The Anacoda environment comes with a large number of pre-installed packages and a conda package handling tool, which will allow you to easily install all the other necessary packages. Packages that correspond to the NumPy, Matplotlib, and Pandas libraries will be installed by default (they are part of the environment), while the TensorFlow and Transformers libraries will need to be installed separately. For the TensorFlow library, we suggest that you follow the installation guidelines that are available on the official library page or on the conda community page. You can find the Transformers Library installation guidelines on the library's website at this address.
Materials from the repository can also be executed in the cloud within the Google Colab platform. Google Colab is a platform that offers a configured environment for work, which means that if you opt for this option, you won't need to use a local machine or install anything. All materials in the repository have a badge at the very beginning with the inscription Open in Colab, which looks like this
. All you have to do is click on it to open the content in this environment. In order to use Google Colab, you need a Google account.
In the next lesson, we will introduce the Google Colab platform and some of its basic functionalities, while in the last lesson of this section, we will introduce you to the functionalities of the NumPy library and remind you to use the Matplotlib library.
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