1. Advanced-Data Structures: Data Structures are important elements of python, can say building blocks of python. These are significantly used to store the data in an organized way and provides easy access as well as modification to the data. They can relate data among themselves and can perform a number of operations/tasks on them within python.
2. NumPy: It is an integrable part of developing integral parts of ML. It allows us to create N-dimensional array objects, perform linear algebra, and much more. Most deep learning libraries use NumPy as their model datatype for tensors.
3. Pandas: It is a manipulation of data and analysis of the library. It is commonly used to create a specific data structure called DataFrame out of CSV/excel files. We can access data through it. DataFrame allows us to store and manipulate the tabular form of data in rows and columns wrapped with observation and variable respectively.
3. Matplotlib: It is a python library used for plotting/visualizing the data either in a 2D plot or a 3D plot as well. Extremely it is used in plotting 2D plot in different ML models through the command (import matplotlib.pyplot). It includes multiple plots such as bar graph, pie chart, histogram, scatter plot, area plot, etc.
3. Seaborn: Another data visualizing library that is better than Matplot. This plots the data in a different form which is more eye supportive to visualize.