Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python: Get ready for AI, ML, and DL with NumPy, SciPy, Pandas, and Matplotlib stack

Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science. In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression. In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems. Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib. Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function. Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing. Finally, we will also cover some basics of machine learning that will help us start our deep learning journey. By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.

This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms. One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.