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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-science-and-scikit-learn-in-python
课程评论：没有评论
课程名称：Python中的数据科学与scikit-learn入门 课程概述：本课程旨在教会您如何利用Python和人工智能的强大功能来创建和测试假设。我们将从基础开始，学习一些基本的Python数据科学知识，然后深入其丰富的应用来测试所创建的假设。课程将介绍一些重要的探索性数据分析（EDA）和机器学习库，如Numpy、Pandas和Scikit-learn。在学习了线性回归背后的基本理论（和数学）后，我们将完整地经历读取数据、清理数据及应用回归模型以评估糖尿病进展的流程。课程结束时，您将能够应用分类模型预测患者健康数据中心脏病的有无。 课程大纲： 1. **Python编程基础与假设测试** - 描述：在本模块中，我们将开始学习Python编程。在熟悉Python和Jupyter Notebook界面后，我们将深入一些基本的编程范式，如变量、循环和函数。我们还将涵盖列表和字典等数据结构。我们会学习Python最有用的一部分 - 有效导入和使用模块。最后，我们将介绍scikit-learn，并通过一个分类问题预测健康数据中癌症的有无。 2. **创建假设：Numpy、Pandas和Scikit-Learn** - 描述：在本模块中，我们将熟悉数据科学中两个最重要的包：Numpy和Pandas。我们将首先学习这两个包的区别，然后熟悉np数组及其功能。添加文本将我们的数组转变为表格，进而引入Pandas模块。在基本介绍后，我们将学习一系列重要的数据处理工具，如索引、数据集的合并与组合以及数据重塑。 3. **重温Scikit-Learn：假设测试的机器学习** - 描述：在本模块中，我们将从头开始构建和测试假设，学习理论与代码，我们将学习通过不同类型的机器学习算法来测试我们的预测。我们将首先经历一些必要的数据预处理步骤，让自己了解数据。熟悉使用Scikit-Learn库从查阅文档开始。之后，我们将加载一个数据集并分析其一些基本属性。最后，我们将导入和使用模型进行预测。 4. **使用分类预测心脏病的存在** - 描述：在最后的项目中，我们将尝试使用患者数据预测心脏病的存在。我们将加载数据，创建新特征，并应用使用scikit-learn的机器学习算法。
Name:Introduction to Python Programming for Hypothesis Testing
Description:In this module, we'll get ourselves started with Programming in Python. After becoming familiar with Python and the Jupyter Notebook interface, we'll dive into some basic coding paradigms such as variables, loops, and functions. We'll also cover data structures in the form of lists and dictionaries. We'll go through one of the most useful things in your Python arsenal - importing and using modules effectively. Finally, we'll introduce scikit-learn and walk through a classification problem to predict the presence/absence of cancer from health data.
Name:Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn
Description:In this module, we'll become familiar with the two most important packages for data science: Numpy and Pandas. We'll begin by learning the differences between the two packages. Then, we'll get ourselves familiar with np arrays and their functionalities. Adding text turns our arrays into tables, and gives rise to the Pandas module. After a basic introduction, we'll end with a series of important data manipulation tools such as indexing, merging/combining datasets, and reshaping data.
Name:Scikit-Learn Revisited: ML for Hypothesis Testing
Description:In this module, we'll work from the ground up to build and test our hypothesis. Learning both the theory and the code, we'll learn to test our predictions with different types of machine learning algorithms. We'll start by going through some of the necessary data preprocessing steps to orient ourselves. Getting familiar with using the Scikit-Learn library starts with the documentation. From there, we'll load in a dataset and analyze some of its most basic properties. Finally, we'll import and use models to make a prediction.
Name:Using Classification to Predict the Presence of Heart Disease
Description:In the final project, we'll try and predict the presence of heart disease using patient data. We'll load in data, create new features, and apply a machine learning algorithm using scikit-learn.
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data.
