Bøger af Wei-Meng Lee

DESCRIPTIONStatistics is an important skill set to have when working as a quality analyst, a mathematician, a data analyst, a software engineer, or any analytical job. This book, "Implementing Statistics with Python," will teach you the basics of statistics and how to use Python to analyze data. You will learn to find patterns, quantify uncertainty, and make data-driven predictions with confidence. You will start with basic statistics and then use Python libraries like NumPy and Pandas for data manipulation. You will also learn data visualization with Matplotlib and Seaborn to create informative charts. The book covers probability theory and statistical inference to help you make data-driven decisions. You will be exploring regression and time series analysis with ARIMA for forecasting. Finally, the book introduces ML algorithms, preparing you for real-world data science projects.The book focuses on applying statistics rather than theory, using popular libraries like NumPy, SciPy, Pandas, Matplotlib, and Scikit-Learn. Reading this book will give you a good foundation for working with ML, business analytics, and data-driven business challenges.KEY FEATURES ● Learn the various aspects of statistics and its applications in real-world scenarios.● Learn about the various libraries in Python for working with data.● Adopt the learn-by-doing approach to solve real-world statistics problems.● Learn how statistics is applied to Machine Learning.WHAT YOU WILL LEARN● Learn the fundamentals of Python and its libraries like Numpy, Pandas, Matplotlib and Seaborn.● Grasp descriptive statistics and probability concepts.● Perform statistical inference with Chi-square, ANOVA, and regression analysis.● Skillfully navigate multivariate and time series analysis.● Apply statistical techniques in practical ML.WHO THIS BOOK IS FORThis book is for readers with basic Python knowledge who want to apply statistics in real-life scenarios, and those pursuing careers in data analytics, data engineering, data science, ML, and AI. It is also ideal for students beginning a course in statistics.

DuckDB is an open source in-process database created for OLAP workloads. It provides key advantages that separate this database from more mainstream OLAP solutions, including embeddability, compatibility with SQL, optimization for fast and efficient analytics, and integration with Python. This practical book shows you how DuckDB leverages Python libraries and tools for data analytics, machine learning, and AI. Author Wei-Meng Lee shows developers, data engineers, data analysts, and data scientists how to get started. You'll learn the primary features and functions of DuckDB, explore use cases and best practices, and examine practical examples of how DuckDB can be used for a variety of data analytics tasks. You'll also dive into specific topics including how to import data into DuckDB, work with tables, perform exploratory data analysis, visualize DuckDB data, perform spatial analysis, and use DuckDB with JSON files, Polars, and JupySQL. You'll also explore: The purpose of DuckDB and its main functions How to conduct data analytics tasks using DuckDB Methods for integrating DuckDB with pandas, Polars, and JupySQL How to use DuckDB to query your data Ways to perform spatial analytics using DuckDB's spatial extension How to work with a diverse range of data including Parquet, CSV, and JSON Wei-Meng Lee is a technologist and founder of Developer Learning Solutions, a company that provides hands-on training on the latest technologies.

Beginning-Intermediate user level

Practical .NET 2.0 Networking Projects demonstrates some of the key networking technologies that are being made easily accessible through .NET Framework 2.0. It discusses communication between wired machines and between networks and mobile devices. The book teaches you about the technologies by walking you through sample projects in a straightforward and direct way.The book begins by discussing background theory so you'll get comfortable with the layout of the .NET Framework and Compact Framework from a networking perspective. Then you'll use the APIs within these frameworks to build a variety of cutting-edge networking applications that cover everything from Bluetooth and RFID communication to sockets programming and chat servers. You'll build working examples for each project, which you can also customize and use for your own purposes. The featured projects cover: Basic introduction to network programming in .NET 2.0 Sockets programming Serial communication Bluetooth and GPS Infrared networking to mobile devices RFID

Sudoku is amazingly popularThis is a beginning programmer's guide with a gaming slant. It mixes learning and funIt teaches the reader how to build a fun, complex, and addictive puzzle game

Go ist eine der neueren Programmiersprachen, die Softwareentwickler in ihrer Werkzeugkiste haben sollten. Die Sprache wurde mit dem Ziel entwickelt, besonders gut lesbar, benutzerfreundlich und effizient zu sein, und ist eine gute Wahl für Entwickler, die Anwendungen für die Cloud erstellen. Dieses Buch zeigt Ihnen die ersten Schritte in Go. Steigen Sie ein und beginnen Sie mit dem Schreiben von Code, der Daten aus der Cloud holt und sie in die Hände der Benutzer legt.

Python makes machine learning easy for beginners and experienced developersWith computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart--it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today.Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.* Python data science--manipulating data and data visualization* Data cleansing* Understanding Machine learning algorithms* Supervised learning algorithms* Unsupervised learning algorithms* Deploying machine learning modelsPython Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.

MOST XML BOOKS in the market today fall into two main categories: They either focus just on the theoretical aspects of the language, or they contain everything related to XML and weigh more than five kilograms! When we first taught XML to beginners, we wanted to find a book that covered just enough ofXML without overwhelming them. We looked for a book that covered not just the fundamen- tals ofXML, but one that also contained the most importantXML-related technologies. Not only that, the book had to illustrate some of the practical uses ofXML in the real world. We.realized that we had to produce a book that targeted beginning to intermediate XML programmers. As trainers, we know the importance of a book that explains concepts clearly and contains concrete code examples illustrating the theory just explained. We carefully reviewed the various XML technologies and specifications that are available today, and in the end, we decided on the most common XML technologies in use today: XPath, XSLT, DOM, SAX, XML in SQL Server, WML, SOAP and XML Schema.

This handy reference provides a quick tour of the .NET compact framework and includes several working projects for getting productive straightaway.