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Hands-On Python Intermediate: A Comprehensive Guide for Advanced Python Programming

Clark Campbell

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Published in Hands On Python INTERMEDIATE: With 50 Exercises 2 Projects 2 Assignments Final Exam

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Python has emerged as one of the most popular programming languages in the world. It is widely used in a variety of fields, including data science, machine learning, web development, and automation. If you have a basic understanding of Python and are ready to take your skills to the next level, then this guide is for you.

Hands-On Python INTERMEDIATE: with 50 Exercises, 2 Projects, 2 Assignments & Final Exam

by Musa Arda

4 out of 5

Language	:	English
File size	:	23880 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	603 pages

What You Will Learn in This Guide

This guide will teach you a wide range of advanced Python concepts and techniques. You will learn how to:

Use NumPy for scientific computing
Analyze data with Pandas
Visualize data with Matplotlib and Seaborn
Train machine learning models with Scikit-learn
Develop web applications with Flask

Prerequisites

Before you start this guide, you should have a basic understanding of Python. You should be familiar with the following concepts:

Variables
Data types
Control flow
Functions
Object-oriented programming

Getting Started

To get started, you will need to install the following Python libraries:

NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn
Flask

You can install these libraries using the following command:

pip install numpy pandas matplotlib seaborn scikit-learn flask

Once you have installed the necessary libraries, you are ready to start learning!

Chapter 1: NumPy

NumPy is a library for scientific computing in Python. It provides a powerful N-dimensional array object and useful linear algebra, Fourier transform, and random number capabilities.

In this chapter, you will learn how to use NumPy to:

Create and manipulate arrays
Perform mathematical operations on arrays
Use linear algebra functions
Use random number generators

Chapter 2: Pandas

Pandas is a library for data analysis and manipulation in Python. It provides data structures and operations for manipulating numerical tables and time series.

In this chapter, you will learn how to use Pandas to:

Create and manipulate DataFrames
Perform data cleaning and transformation
Group and aggregate data
Visualize data

Chapter 3: Matplotlib and Seaborn

Matplotlib is a library for creating 2D plots in Python. Seaborn is a library that builds on Matplotlib to provide a high-level interface for creating informative and visually appealing statistical graphics.

In this chapter, you will learn how to use Matplotlib and Seaborn to:

Create different types of plots
Customize plots
Create interactive plots

Chapter 4: Scikit-learn

Scikit-learn is a library for machine learning in Python. It provides a set of efficient tools for data mining and data analysis.

In this chapter, you will learn how to use Scikit-learn to:

Train and evaluate machine learning models
Use different machine learning algorithms
Deploy machine learning models

Chapter 5: Flask

Flask is a web framework for Python. It is designed to be simple, flexible, and scalable.

In this chapter, you will learn how to use Flask to:

Create web applications
Handle HTTP requests and responses
Use templates to render dynamic content
Deploy web applications

This guide has provided you with a comprehensive overview of advanced Python concepts and techniques. You have learned how to use NumPy for scientific computing, Pandas for data analysis and manipulation, Matplotlib and Seaborn for data visualization, Scikit-learn for machine learning, and Flask for web development.

I encourage you to continue learning and practicing Python. The more you use Python, the more proficient you will become. I hope this guide has helped you take your Python skills to the next level.

Hands-On Python INTERMEDIATE: with 50 Exercises, 2 Projects, 2 Assignments & Final Exam

by Musa Arda

4 out of 5