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所在平台: Udemy |
课程主页: https://www.udemy.com/course/pro-data-science-in-python/
课程评论：没有评论
课程名称：Python专业数据科学 课程概述：该课程探讨了每位数据科学从业者应熟悉的多种数据科学和机器学习技术。课程主要围绕四个核心领域展开：利用Pandas和Matplotlib进行数据处理、使用Keras进行深度学习、采用Scikit-learn进行机器学习、以及通过Statsmodels进行统计分析。本课程深入探讨了这四个主题的基本概念，并向学生展示了当今可以解决的问题。本课程专注于这些技术的计算和实用影响，假设学生对统计学、机器学习和数据科学有一定的了解，或者愿意通过理论材料补充相关知识。学员需要具备Python编程经验，课程仅对如何定义类进行简要讲解（因为整个课程中将使用这些类）。 教学策略是简要说明这些技术背后的理论，展示这些技术在简单问题中的应用，最后向学生呈现一些实际案例。我相信这些真实案例为学生增加了极大的价值，有助于理解这些技术为何如此普遍使用（因为它们解决了实际问题！）。我们将解决的一些案例包括：预测美国GDP、预测伦敦新房价格、识别图片中的方形和三角形、使用在线数据预测车辆价值、检测短信中的垃圾信息等。 简而言之，本课程将教授如何： - 定义类以更好地存储数据 - 绘制数据 - 使用Pandas合并、透视、子集和分组数据 - 通过Statsmodels进行线性回归 - 使用Statsmodels处理时间序列/进行预测 - 实施多种无监督机器学习技术，如聚类 - 应用多种监督技术，如随机森林、分类树、朴素贝叶斯分类器等 - 使用Keras定义深度学习架构 - 设计不同的神经网络，如递归神经网络、多层感知机等 - 使用机器学习分类音频/声音，类似于Alexa、Siri和Cortana的功能 学生需要对统计学、Python和一些机器学习概念有一定的了解。
This course explores several data science and machine learning techniques that every data science practitioner should be familiar with. Fundamentally, the course pivots over four axis: Pandas and Matplotlib for working with dataKeras for Deep Learning, Scikit-learn for machine learningStatsmodels for statistics This course explores the fundamental concepts in these big four topics, and provides the student with an overview of the problems that can be solved nowadays. I only focus on the computational and practical implications of these techniques, and it is assumed that the student is partially familiar with Statistics-ML-Data Science - or is willing to complement the techniques presented here with theoretical material. Python programming experience will be absolutely necessary, as we only explain how to define Classes in Python (as we will use them along the course) The teaching strategy is to briefly explain the theory behind these techniques, show how these techniques work in very simple problems, and finally present the student with some real examples. I believe that these real examples add an enormous value to the student, as it helps understand why these techniques are so used nowadays (because they solve real problems!) Some examples that we will attack here will be: Forecasting the GDP of the United States, forecasting London new houses prices, identifying squares and triangles in pictures, predicting the value of vehicles using online data, detecting spam on SMS data, and many more! In a nutshell, this course explains how to: Define classes for storing data in a better wayPlotting dataMerging, pivoting, subsetting, and grouping data via PandasUsing linear regression via StatsmodelsWorking with time series/forecasting in StatsmodelsSeveral unsupervised machine learning techniques, such as clusteringSeveral supervised techniques such as random forests, classification trees, Naive Bayes classifiers, etcDefine Deep Learning architectures using KerasDesign different neural networks such as recurrent neural networks, multi-layer perceptrons,etc.Classify Audio/sounds in a similar way that Alexa, Siri and Cortana do using machine learning The student needs to be familiar with statistics, Python and some machine learning concepts
