Useful Python Libraries
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Ultimate Python Libraries for Data Analysis and Visualization
Author: Abhinaba Banerjee
language: en
Publisher: Orange Education Pvt Ltd
Release Date: 2024-04-04
Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index
Network Programming in Python: The Basic
For programmers who need to use Python for network-related activities and apps KEY FEATURES ● Comprehensive coverage of Python 3's improved SSL support. ● Create an asynchronous I/O loop on your own. ● A look at the "asyncio" framework, which is included with Python 3.4. DESCRIPTION This book includes revisions for Python 3 as well as all of the classic topics covered, such as network protocols, network data and errors, email, server architecture, and HTTP and web applications. ● Comprehensive coverage of Python 3's improved SSL support. ● How to create an asynchronous I/O loop on your own. ● A look at the "asyncio" framework, which is included with Python 3.4. ● The Flask web framework's URL-to-Python code connection. ● How to safeguard your website from cross-site scripting and cross-site request forgery attacks. ● How Django, a full-stack web framework, can automate the round journey from your database to the screen and back. WHAT YOU WILL LEARN ● Asynchronous models and socket-based networks ● Monitor distant systems using Telnet and SSH connections ● Interact with websites using XML-RPC, SOAP, and REST APIs ● Configure virtual networks in various deployment scenarios ● Analyze security weaknesses in a network WHO THIS BOOK IS FOR This book is for Python programmers who need a thorough understanding of how to use Python for network-related activities and applications. This book covers all you need to know about web application development, systems integration, and system administration. TABLE OF CONTENTS 1. Client- Server Networking: An Overview 2. UDP(User Datagram Protocol) 3. Transmission control protocol (TCP) 4. Domain name system & socket names 5. Data and Errors on the Internet 6. SSL/TLS 7. Architecture of the Server 8. Message Queues and Caches 9. HTTP Clients 10. Servers that handle HTTP 11. www (world wide web) 12. E-mail Construction And Parsing 13.Simple Mail Transfer Protocol(SMTP) 14. Post Office Protocol (POP) 15. Internet Message Access Protocol (IMAP) 16. SSH and Telnet 17. File Transfer Protocol (FTP) 18. Remote Procedure Call (RPC)
Learn AI with Python
Build AI applications using Python to intelligently interact with the world around you. KEY FEATURES ● Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. ● Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. ● Covers deep learning models such as R-CNN and YOLO for object recognition and teaches how to build an image classifier using CNN. DESCRIPTION The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems. WHAT YOU WILL LEARN ● Learn to implement various machine learning and deep learning algorithms to achieve smart results. ● Understand how ML algorithms can be applied to real-life applications. ● Explore logic programming and learn how to use it practically to solve real-life problems. ● Learn to develop different types of artificial neural networks with Python. ● Understand reinforcement learning and how to build an environment and agents using Python. ● Work with NLTK and build an automatic speech recognition system. WHO THIS BOOK IS FOR This book is for anyone interested in learning about artificial intelligence and putting it into practice with Python. This book is also valuable for intermediate Machine Learning practitioners as a reference guide. Readers should be familiar with the fundamental understanding of Python programming and machine learning techniques. TABLE OF CONTENTS 1. Introduction to AI and Python 2. Machine Learning and Its Algorithms 3. Classification and Regression Using Supervised Learning 4. Clustering Using Unsupervised Learning 5. Solving Problems with Logic Programming 6. Natural Language Processing with Python 7. Implementing Speech Recognition with Python 8. Implementing Artificial Neural Network (ANN) with Python 9. Implementing Reinforcement Learning with Python 10. Implementing Deep Learning and Convolutional Neural Network