Applied Machine Learning in Python

开始时间: 04/22/2022 持续时间: Unknown

所在平台: CourseraArchive

课程类别: 计算机科学

大学或机构: CourseraNew




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This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Python中的应用机器学习:本课程将向学习者介绍应用机器学习,将重点更多地放在技术和方法上,而不是这些方法背后的统计数据。本课程将首先讨论机器学习与描述性统计的不同之处,并通过教程介绍scikit学习工具包。将讨论数据的维数问题,并将处理对数据进行聚类以及评估这些聚类的任务。将描述用于创建预测模型的监督方法,学习者将能够应用scikit学习预测建模方法,同时了解与数据概化性相关的过程问题(例如,交叉验证,过度拟合)。本课程将以更先进的技术作为结尾,例如建筑合奏以及预测模型的实际局限性。到本课程结束时,学生将能够识别出监督(分类)技术和非监督(聚类)技术之间的区别,确定他们需要将哪种技术应用于特定的数据集和需求,满足这些需求的工程师特征,以及编写python代码进行分析。 本课程应在Python数据科学导论及应用绘图,制图和绘图之后进行。 Python中的数据表示以及Python中的应用文本挖掘和Python中的应用社会分析之前的数据表示。


This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.





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