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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-models-in-science
课程评论：没有评论
课程名称：科学中的机器学习模型 课程概述：本课程旨在帮助任何有兴趣将机器学习技术应用于科学问题的人。我们将学习完整的机器学习流程，从数据读取、清洗和转换，到运行基本和高级的机器学习算法。课程将从数据预处理技术入手，如主成分分析（PCA）和线性判别分析（LDA）。随之，我们将深入学习基础的人工智能算法：支持向量机（SVM）和K均值聚类。与此同时，我们将构建数学和编程工具箱，为更复杂的模型打下基础。最后，我们将探索诸如随机森林和神经网络等高级方法。在整个学习过程中，我们将使用医学和天文数据集。在课程的最后项目中，我们将应用我们的技能，通过Python比较不同的机器学习模型。 课程大纲： 1. **数据准备与预处理** 本模块将介绍在使用AI算法之前需要进行的步骤，包括填补缺失值和删除异常值等数据预处理技术。将深入探讨PCA与LDA的维度减少方法，并学习如何在Python中编写算法，以便为下一模块中的数据使用做准备。 2. **基础人工智能算法：K均值与支持向量机** 本模块将深入探讨两种基础的机器学习算法：K均值与支持向量机。我们将比较监督学习和非监督学习的两个分支，并深入了解K近邻分类与K均值聚类的相似性与差异，最终在Python中实现这两种算法。 3. **高级人工智能：神经网络与决策树** 本模块将探索一些高级AI技术，首先关注以随机森林广为人知的树基算法，然后逐步过渡至神经网络，从不同模型的实验开始。在Tensorflow Playground中熟悉神经网络的不同机制，并最终编写自己的神经网络模型，以对未见数据进行预测。 4. **课程项目** 在本模块中，我们将开展一个课程项目，通过健康数据预测糖尿病。我们将实施不同的回归模型，并检查测试集上的误差以进行比较。
Name:Before the AI: Preparing and Preprocessing Data
Description:In this module, we'll tackle the steps taken before we can use AI algorithms. We'll start with an introduction to the most prominent data preprocessing techniques including filling in missing values and removing outliers. Then we'll dive into data transformations including PCA and LDA, two methods featured heavily for dimensionality reduction. Finally, we'll learn how to code the algorithms in Python to set up your data for use in the next module.
Name:Foundational AI Algorithms: K-Means and SVM
Description:In this module, we'll dive into two of the most foundational machine learning algorithms: K-Means and support vector machines. We'll start by comparing the two branches of ML: supervised and unsupervised learning. Then, we'll go into the specific similarities and differences between K-Nearest neighbors for classification and K-Means clustering. Finally, we'll perform deep dives into K-Means and SVMs, learning the basic theory behind them and how to implement each in Python.
Name:Advanced AI: Neural Networks and Decision Trees
Description:In this module, we'll explore some advanced AI techniques. We'll start with tree-based algorithms, made popular because of the use of random forests for both classification and regression. Then, we'll build our way to neural networks, starting from experimentation on the different models. We'll spend some time in the Tensorflow playground getting familiar with the different mechanics behind neural networks. Finally, we'll code our own neural networks to make predictions on unseen data.
Name:Course Project
Description:In this module, we'll go through a course project to predict diabetes from health data. We'll compare different regressors by implementing them and checking the error on a test set.
This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models. Finally, we'll explored advanced methods such as random forests and neural networks. Throughout the way, we'll be using medical and astronomical datasets. In the final project, we'll apply our skills to compare different machine learning models in Python.
