Python basics

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• [ ] Data Types
• [ ] I&O
• [ ] Plots

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Data Types: Data Types are an essential concept in Python and form the building blocks of all programming languages. In Python, there are several built-in data types that we can use to store and manipulate different types of data. The most common data types in Python include integers, floating-point numbers, strings, booleans, lists, tuples, and dictionaries. Understanding data types is crucial for writing effective code for AI applications.

Input and Output (I&O): Input and Output (I&O) refer to the ways in which we can receive information into our Python programs and the ways in which we can output data. In Python, we use the input() function to receive input from the user, and the print() function to display output on the screen. We can also read from and write to files, which is an essential aspect of data handling in AI. Understanding I&O is critical to building effective Python programs that interact with users and process data.

Plots: Plots are graphical representations of data that allow us to visualize data and make insights. In Python, we can use several libraries, such as matplotlib, seaborn, and plotly, to create various types of plots, including scatter plots, line plots, bar charts, and histograms. Plots are a crucial tool in AI for visualizing data and results, and they help us to communicate insights effectively. Understanding how to create plots in Python is essential for building effective AI applications.

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• [ ] Lists: Lists are used to store a collection of items of any data type. In AI/ML, we often work with datasets that contain multiple rows and columns of data. We can store each row of data as a list and create a list of lists to represent the entire dataset. We can also use lists to store the predictions generated by a model and compare them to the actual labels.

• [ ] NumPy Arrays: NumPy is a Python library for working with arrays of data. NumPy arrays are similar to lists but are optimized for performing numerical operations. In AI/ML, we often work with large datasets, and NumPy arrays can significantly improve the performance of our code. NumPy arrays are also used to represent tensors, which are the basic data structure used in deep learning.

• [ ] Tuples: Tuples are similar to lists, but they are immutable, which means we cannot modify their contents once they are created. Tuples are useful in AI/ML for representing fixed data structures, such as the structure of a neural network, where the number of layers and neurons in each layer is predetermined.

• [ ] Dictionaries: Dictionaries are used to store key-value pairs. In AI/ML, we can use dictionaries to store the parameters of a machine learning model, where the keys are the names of the parameters, and the values are the values of the parameters.

• [ ] Strings: Strings are used to represent text data. In AI/ML, we often work with text data, such as natural language processing tasks. Strings are used to store text data, preprocess the data, and represent the output of a model.

• [ ] Booleans: Booleans are used to represent true or false values. In AI/ML, booleans are used to represent binary outcomes, such as the success or failure of a model.

• [ ] Sets: Sets are used to store unique elements. In AI/ML, sets are not commonly used, but they can be used to remove duplicates from a dataset or set of predictions.