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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-numpy-tutorial-for-beginners/
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**课程名称：** Python NumPy 入门教程 **课程概述：** 这门课程专为希望提升数据科学职业生涯的初学者设计，重点介绍 Python NumPy 库。NumPy (Numerical Python) 是 Python 中最受欢迎的机器学习库之一，它功能强大、易于使用，并包含丰富的数学和科学计算工具，可以轻松实现复杂的数学运算。 NumPy 允许用户创建多维矩阵和数组，是 MATLAB 的一个优秀替代品。它跨平台，并经常与 Matplotlib 和 SciPy 等库协同工作。 本课程将重点解析 NumPy 数组与 Python 列表的区别： * **性能：** NumPy 数组比 Python 列表更**快**，并且**内存占用更少**，因为数据存储在连续的内存块中。 * **数据类型：** Python 列表可以包含多种数据类型，而 NumPy 数组**仅允许单一数据类型**，这一点对于执行数学运算至关重要。 **课程重点内容：** * NumPy 数组介绍 * 安装 NumPy 库的步骤 * 创建 NumPy 数组 * NumPy 的数据类型 * NumPy 数组的索引 * NumPy 数组的切片 * NumPy 数组函数 * NumPy 的广播机制 * NumPy 数组操作 * 遍历 NumPy 数组 **课程目标：** 完成本课程后，您将掌握 NumPy 的核心概念和常用操作，为进一步学习数据科学和机器学习打下坚实基础。
In this course, you will learn about Python Numpy which is for beginners who want to enhance their career in Data Science. Numpy is one of the most popular machine learning libraries in Python. NumPy stands for Numerical Python. Numpy is very interactive and easy to use. It has a huge collection of advanced mathematical and scientific computing package. We can Make complex mathematical implementations very easily. It lets you create multidimensional matrices and arrays. Numpy is an alternative to MATLAB. NumPy is cross-platform, We often use it with packages like Matplotlib and SciPy.Numpy is initialized in the form of arrays. We all know that we don't use arrays in python instead we use lists in python. However, Numpy arrays are faster than Python lists. Let's see how it differs from python lists. NumPy uses much less memory to store data than Python lists and these data are stored at one continuous place in memory. Python lists contain single or different data types within a single list. However, the NumPy array allows only a single datatype. The mathematical operations can be performed on arrays only if the arrays are homogenous. By the end of the course, you will learn the following topicsIntroduction to Numpy ArraysSteps to Install Numpy LibraryHow to Create a Numpy Array?Datatypes in Numpy ArrayIndexing in Numpy ArraySlicing in in Numpy ArrayFunctions in Numpy ArrayBroadcasting in Numpy ArrayNumpy Array ManipulationIterating Numpy ArrayCourse Summary
