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所在平台: Udemy |
课程主页: https://www.udemy.com/course/real-data-science-problems-with-python/
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课程名称：使用Python处理真实数据科学问题 课程概述： 本课程探讨了多种机器学习和数据科学技术，通过使用来自多个来源的真实数据集/图像/音频进行学习。这些真实的情境相较于虚拟示例更具挑战性，促使学生更深入地思考问题，更有效地预处理数据，以及从不同角度评估预测性能。所用数据集来自Kaggle、美国Data.gov、CrowdFlower等多个平台。每节课都展示了如何预处理数据，采用合适的技术建模，以及评估每种技术在特定问题上的表现。某些讲座包含多种技术，我们会讨论哪种技术的表现更优。所有代码均已共享，如有疑问，学生可直接联系我。此外，每节课都可以下载，方便学生在旅行中学习。学生应对Python及一些数据科学技术有一定了解。虽然每节课中会讨论一些技术细节，但不会深入解释每种方法背后的数学原理。 本课程展示的一些技术包括： - 使用OpenCV进行纯图像处理 - 使用Keras-Theano的卷积神经网络 - 逻辑回归和朴素贝叶斯分类器 - Adaboost、支持向量机用于回归和分类，随机森林 - 实时视频处理，多层感知机，深度神经网络等 - 线性回归，惩罚估计量，聚类，主成分分析 所用模块/库包括： - Scikit-learn - Keras-theano - Pandas - OpenCV 课程中使用的一些真实案例： - 基于社会经济变量预测GDP - 在图像中检测人体部位和手势 - 实时视频中的对象追踪 - 机器学习用于语音识别 - 检测短信中的垃圾信息 - 使用推特数据进行情感分析 - 计算图像中的对象并检索其位置 - 预测伦敦房价 - 根据美国人口普查数据预测是否赚取超过5万的收入 - 预测美国核反应堆的核能输出 - 预测某些美国县的房价 - 及更多其他案例 课程的动力在于，许多希望学习数据科学/机器学习的学生通常会被不够具有挑战性的虚拟数据集所困扰。本课程旨在帮助学生在理论学习与实际应用之间架起桥梁，让他们能够在真实情境中进行机器学习。
This course explores a variety of machine learning and data science techniques using real life datasets/images/audio collected from several sources. These realistic situations are much better than dummy examples, because they force the student to better think the problem, pre-process the data in a better way, and evaluate the performance of the prediction in different ways. The datasets used here are from different sources such as Kaggle, US Data.gov, CrowdFlower, etc. And each lecture shows how to preprocess the data, model it using an appropriate technique, and compute how well each technique is working on that specific problem. Certain lectures contain also multiple techniques, and we discuss which technique is outperforming the other. Naturally, all the code is shared here, and you can contact me if you have any questions. Every lecture can also be downloaded, so you can enjoy them while travelling. The student should already be familiar with Python and some data science techniques. In each lecture, we do discuss some technical details on each method, but we do not invest much time in explaining the underlying mathematical principles behind each method Some of the techniques presented here are: Pure image processing using OpencCVConvolutional neural networks using Keras-TheanoLogistic and naive bayes classifiersAdaboost, Support Vector Machines for regression and classification, Random ForestsReal time video processing, Multilayer Perceptrons, Deep Neural Networks,etc.Linear regressionPenalized estimatorsClusteringPrincipal components The modules/libraries used here are: Scikit-learnKeras-theanoPandasOpenCV Some of the real examples used here: Predicting the GDP based on socio-economic variablesDetecting human parts and gestures in imagesTracking objects in real time videoMachine learning on speech recognitionDetecting spam in SMS messagesSentiment analysis using Twitter dataCounting objects in pictures and retrieving their positionForecasting London property pricesPredicting whether people earn more than a 50K threshold based on US Census dataPredicting the nuclear output of US based reactorsPredicting the house prices for some US countiesAnd much more. The motivation for this course is that many students willing to learn data science/machine learning are usually suck with dummy datasets that are not challenging enough. This course aims to ease that transition between knowing machine learning, and doing real machine learning on real situations.
