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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-algebra-and-feature-selection-in-python/
课程评论：没有评论
课程名称：Python中的线性代数与特征选择 课程概述： 你是否想学习线性代数？恭喜你来到了正确的地方！首先，我们要祝贺你意识到掌握这项技能的重要性。无论你想追求数据科学、人工智能工程、机器学习、数据分析、软件工程还是统计学的职业，了解如何应用线性代数都是必不可少的。 本课程将帮助你成为一名能理解算法基础数学的专业人士，而不仅仅是盲目应用算法。课程将为你提供数据科学与统计分析两个基本部分的理论和实践基础——线性代数和降维。 线性代数在数据科学与人工智能课程中常常被忽视，虽然它至关重要。许多讲师往往侧重于特定框架的实际应用，而没有从基础出发，这导致知识的空白和理解的缺失。在本课程中，你将有机会夯实基础，从而更好地理解复杂的机器学习和人工智能主题。 课程首先介绍基本的代数概念，如向量、矩阵、单位矩阵、向量的线性组合等。我们将利用这些知识来解决实际的线性方程、判断一组随机向量的线性独立性，以及计算特征向量和特征值，为接下来的降维学习做好准备。 降维的概念在数据科学、统计分析和机器学习中至关重要，特别是在当今数据驱动的时代，能够识别数据集中重要特征的能力变得尤为重要。想象一下，你的数据中有成百上千的属性，处理这样复杂的信息可能导致多种问题——训练时间缓慢，可能的多重共线性，维度诅咒，甚至模型过拟合。降维可以帮助你避免所有这些问题，通过选择实际包含重要信息的数据部分并忽略影响较小的部分。 课程将讨论两种基础的降维技术——主成分分析（PCA）和线性判别分析（LDA）。这些方法会转化你所处理的数据，并创建承载大部分方差的新特征。你将学习PCA和LDA的理论，并通过两个完整的Python示例，看到数据转换的实际过程。我们将分别进行PCA和LDA的逐步演示，最后比较这两种算法在速度和准确性上的表现。 我们投入了大量精力来确保本课程成为任何希望成为数据分析师、数据科学家、机器学习工程师或人工智能工程师的人的完美基础培训。
Do you want to learn linear algebra?You have come to the right place!First and foremost, we want to congratulate you because you have realized the importance of obtaining this skill. Whether you want to pursue a career in data science, AI engineering, machine learning, data analysis, software engineering, or statistics, you will need to know how to apply linear algebra.This course will allow you to become a professional who understands the math on which algorithms are built, rather than someone who applies them blindly without knowing what happens behind the scenes.But let's answer a pressing question you probably have at this point: "What can I expect from this course and how it will help my professional development?"In brief, we will provide you with the theoretical and practical foundations for two fundamental parts of data science and statistical analysis - linear algebra and dimensionality reduction.Linear algebra is often overlooked in data science and AI courses, despite being of paramount importance. Most instructors tend to focus on the practical application of specific frameworks rather than starting with the fundamentals, which leaves you with knowledge gaps and a lack of full understanding. In this course, we give you an opportunity to build a strong foundation that would allow you to grasp complex ML and AI topics.The course starts by introducing basic algebra notions such as vectors, matrices, identity matrices, the linear span of vectors, and more. We'll use them to solve practical linear equations, determine linear independence of a random set of vectors, and calculate eigenvectors and eigenvalues, all preparing you for the second part of our learning journey - dimensionality reduction.The concept of dimensionality reduction is crucial in data science, statistical analysis, and machine learning. This isn't surprising, as the ability to determine the important features in a dataset is essential - especially in today's data-driven age when one must be able to work with very large datasets.Imagine you have hundreds or even thousands of attributes in your data. Working with such complex information could lead to a variety of problems - slow training time, the possibility of multicollinearity, the curse of dimensionality, or even overfitting the training data.Dimensionality reduction can help you avoid all these issues, by selecting the parts of the data which actually carry important information and disregarding the less impactful ones.In this course, we'll discuss two staple techniques for dimensionality reduction - Principal Components Analysis (PCA), and Linear Discriminant Analysis (LDA). These methods transform the data you work with and create new features that carry most of the variance related to a given dataset. First, you will learn the theory behind PCA and LDA. Then, going through two complete examples in Python, you will see how data transformation occurs in practice. For this purpose, you will get one step-by-step application of PCA and one of LDA. Finally, we will compare the two algorithms in terms of speed and accuracy.We've put a lot of effort into making this course the perfect foundational training for anyone who wants to become a data analyst, data scientist, machine learning engineer, or AI engineer.
