Make a summary of this including tools and what they are used for: Libraries are a collection of functions and methods that allow you to perform many actions without writing the code. We will focus on the following Python libraries: Scientific Computing Libraries in Python Visualization Libraries in Python High-Level- Machine Learning and Deep Learning Libraries (High-level means you don’t have to worry about details making studying or improving difficult.) And finally, Deep Learning Libraries in Python, and Libraries used in other languages Now, scientific computing libraries contain built-in modules providing different functionalities, which you can use directly. They are also called frameworks. For example, Pandas offers data structures and tools for effective data cleaning, manipulation, and analysis. It provides tools to work with different types of data. The primary instrument of Pandas is a two-dimensional table consisting of columns and rows, called a Data Frame. Pandas can also provide easy indexing so you can work with your data. NumPy libraries are based on arrays and matrices, allowing you to apply mathematical functions to the arrays. Pandas is built on top of NumPy. You use data visualization methods to communicate with others and display meaningful results of an analysis. These libraries enable you to create graphs, charts, and maps. The Matplotlib package is the most well-known library for data visualization. They are popular for making graphs and plots, and the graphs are easily customizable. Another high-level visualization library is Seaborn. It is based on matplotlib. This library generates heat maps, time series, and violin plots. Now, for machine learning, the Scikit-learn library contains tools for statistical modeling, including regression, classification, clustering, and so on. It is built on NumPy, SciPy, and matplotlib. It is simple to get started. In this high-level approach, you define the model and specify the parameter types you want to use. For building deep learning models, Keras allows you to build the standard deep learning model. Like Scikit, the high-level interface allows you to build models in a quick, simple manner. It can function using Graphics processing units (GPU) but in many cases, a lower-level environment is necessary for deep learning. TensorFlow is a low-level framework used in the large-scale production of deep learning models. It's designed for production and deployment but can be unwieldy for experimentation. Pytorch is used for experimentation, making it simple for researchers to test ideas. Apache Spark is a general-purpose cluster-computing framework allowing you to process data using compute clusters. The data is processed in parallel in more than one computer simultaneously. The Spark library has similar functionality to the following: Pandas, Numpy, and Scikit-learn. Apache Spark data processing jobs can be in: Python R Scala, and SQL There are many Scala libraries. Scala is predominately used in data engineering and data science. Let’s discuss some libraries that are complementary to Spark. Vegas is a Scala Library for statistical data visualizations. With Vegas, you can work with data files as well as Spark Data Frames. For deep learning, you can use big DL. R has built-in functionality for machine learning and data visualization, but there are also complementary libraries. ggplot2 is a popular library for data visualization in R. You can also use libraries that allow you to interface with Keras and TensorFlow. And R was a de-facto standard for open-source data science, but now Python will supersede it. In this video, you learned that: Libraries usually contain built-in modules providing different functionalities that can be used directly. You can use data visualization methods to communicate with others and display meaningful results of an analysis. For machine learning, the Scikit-learn library contains tools for statistical modeling, including regression, classification, clustering and so on. TensorFlow is a low-level framework used in large-scale production of deep learning models. And, Apache Spark is a general-purpose cluster-computing framework allowing you to process data using compute clusters.