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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/design-thinking-predictive-analytics-data-products
课程评论：没有评论
课程名称：设计思维与数据产品的预测分析 课程概览：本课程是“Python数据产品的预测分析”四门课程专题中的第二门，基于第一课程中涉及的数据处理内容，介绍如何在Python中设计预测模型的基础知识。在本课程中，学员将理解统计学习的基础概念，并学习构建预测模型的多种方法。在每一个专题步骤中，学员将获得数据处理的实践经验，并逐步提升技能，最终以一个综合项目作为课程的总结，涵盖该专题所教的所有概念。 课程大纲： - 第1周：监督学习与回归 - 本周将介绍课程大纲，下载所有课程资料，并确保系统正常运行。在此过程中，学员将了解监督学习和回归的基础知识。 - 第2周：特征 - 本周将学习数据集中的特征，以及如何在Jupyter Notebook中对其进行清理、操作和分析。 - 第3周：分类 - 本周将了解分类的概念以及实现分类的几种方法，例如K近邻、逻辑回归和支持向量机。 - 第4周：梯度下降 - 本周将学习如何正确训练和测试模型的重要性，并分别在Python和TensorFlow中实现梯度下降算法。 - 最终项目 - 在课程的最后一周，学员将继续构建“Python数据产品的预测分析”第一课程中的项目，使用简单的预测机器学习算法。学员需要找到一个数据集，清理数据并进行基本分析。
Name:Week 1: Supervised Learning & Regression
Description:Welcome to the second course in this specialization! This week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of supervised learning and regression.
Name:Week 2: Features
Description:This week, we will learn what features are in a dataset and how we can work with them through cleaning, manipulation, and analysis in Jupyter notebooks.
Name:Week 3: Classification
Description:This week, we will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines.
Name:Week 4: Gradient Descent
Description:This week, we will learn the importance of properly training and testing a model. We will also implement gradient descent in both Python and TensorFlow.
Name:Final Project
Description:In the final week of this course, you will continue building on the project from the first course of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data.
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
