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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-predictive-techniques-with-scikit-learn-tensorflow/
课程评论：没有评论
课程名称：使用 Scikit-Learn 和 TensorFlow 的高级预测技术 课程概述： 在这个课程中，您将学习如何运用集成方法，通过智能结合多个个体预测器的预测，显著提高分类和回归问题的预测准确性。课程将探讨在使用预测分析解决实际问题时需要考虑的多种实用因素，如选择哪些特征、需要多少特征、是否需要创建新特征、如何组合特征以传递相同的底层信息以及应使用哪些超参数等。此外，您将了解人工神经网络，这种模型模仿生物神经网络的工作方式，近年来由于计算能力的提升、方法的改进和软件的增强而重新受到关注，并成为深度学习等高级方法的基础。课程还将介绍如何使用强大的 TensorFlow 库进行深度学习模型的预测分析。 作者介绍： 阿尔瓦罗·富恩特斯是一位数据科学家，拥有定量经济学和应用数学的硕士学位，拥有超过10年的分析工作经验。他曾在危地马拉中央银行担任经济分析师，构建经济和金融数据模型。他创办了Quant Company，提供数据科学领域的咨询和培训服务，并参与多个商业、教育、心理学和大众传媒等领域的项目。阿尔瓦罗还教授过多门在线和现场课程，涵盖数据科学、数学、统计学、R 编程和 Python 等主题。他热爱 Python，并在数据分析和预测中常规使用它，同时也喜欢 R。他对大数据的 Spark 方法充满兴趣，并关注其简化复杂问题的方式。尽管他不是软件工程师或开发人员，但他对网络技术抱有广泛的兴趣，并具备 R 编程、Spark、SQL（PostgreSQL）、Excel、机器学习、统计分析、计量经济学和数学建模等技术技能。在预测分析领域，他拥有丰富的专业和教学经验，并在其咨询实践中使用 Python 工具解决实际问题。 此课程非常适合希望提高预测分析技能并深入了解集成方法与深度学习应用的学习者。
Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions. Artificial Neural Networks are models loosely based on how neural networks work in a living being. These models have a long history in the Artificial Intelligence community with ups and downs in popularity. Nowadays, because of the increase in computational power, improved methods, and software enhancements, they are popular again and are the basis for advanced approaches such as Deep Learning. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library. About the Author: Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as; Business, Education, Psychology and Mass Media. He also has taught many (online and in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python. Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy between what is the "best" R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated things. He is not a software engineer or a developer but is generally interested in web technologies. He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling. Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.
