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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-applications
课程评论：没有评论
课程名称：机器学习：概念与应用 概述：本课程为您提供机器学习理论与实践的全面介绍。您将学习如何使用Python及行业标准库和工具（如Pandas、Scikit-learn和Tensorflow）来获取、探索和准备数据进行建模，并运用各种技术来训练和评估模型。这些技术包括普通最小二乘法线性回归、逻辑回归、支持向量机、决策树及其集成、聚类、主成分分析、隐马尔可夫模型以及深度学习。课程的一个关键特色在于，您不仅学习如何应用这些技术，还会理解其背后的概念，以便掌握其工作原理、过程中的决策及结果的意义。课程还使用真实世界的数据集，主要来源于公共政策领域，基于芝加哥大学研究生的机器学习入门课程，为深入和更专业的学习提供了坚实的基础。 课程大纲： 1. **机器学习与机器学习管道**：介绍机器学习管道及数据建模前的初步工作，学习如何使用Pandas导入数据，并探索线性回归的基础。 2. **最小二乘法与最大似然估计**：深入线性回归模型的评估和特征选择，学习最大似然估计的方法。 3. **基函数与正则化**：学习基函数和多项式扩展，以应对非线性关系，并探讨偏差-方差权衡和正则化方法。 4. **模型选择与逻辑回归**：评估和调优模型，学习交叉验证和网格搜索，掌握逻辑回归的理论与实践。 5. **更多分类器：支持向量机与朴素贝叶斯**：学习支持向量机(SVM)和朴素贝叶斯分类技术。 6. **基于树的模型、集成方法与评估**：掌握决策树分类、集成模型如Bagging、Boosting与随机森林，以及分类器性能评估。 7. **聚类方法**：探讨无监督学习，学习k均值聚类与层次聚类等多种聚类技术。 8. **降维与时序模型**：学习主成分分析进行降维，以及隐马尔可夫模型的应用。 9. **深度学习**：深入理解深度学习，学习前馈神经网络和卷积神经网络的设计、实现和评估，使用Python和Keras进行实践。 本课程为机器学习的核心概念和应用提供了全面的基础，为学员向更高级的研究拓展打下了扎实的基础。
Name:Machine Learning and the Machine Learning Pipeline
Description:In this module you will be introduced to the machine-learning pipeline and learn about the initial work on your data that you need to do prior to modeling. You will learn about how to ingest data using Pandas, a standard Python library for data exploration and preparation. Next, we turn to the first approach to modeling that we explore in this class, linear regression with ordinary least squares.
Name:Least Squares and Maximum Likelihood Estimation
Description:In this module, you continue the work that we began in the last with linear regressions. You will learn more about how to evaluate such models and how to select the important features and exclude the ones that are not statistically significant. You will also learn about maximum likelihood estimation, a probabilistic approach to estimating your models.
Name:Basis Functions and Regularization
Description:This module introduces you to basis functions and polynomial expansions in particular, which will allow you to use the same linear regression techniques that we have been studying so far to model non-linear relationships. Then, we learn about the bias-variance tradeoff, a key relationship in machine learning. Methods like polynomial expansion may help you train models that capture the relationship in your training data quite well, but those same models may perform badly on new data. You learn about different regularization methods that can help balance this tradeoff and create models that avoid overfitting.
Name:Model Selection and Logistic Regression
Description:In this module, you first learn more about evaluating and tuning your models. We look at cross validation techniques that will help you get more accurate measurements of your model's performance, and then you see how to use them along with pipelines and GridSearch to tune your models. Finally, we look a the theory and practice of our first technique for classification, logistic regression.
Name:More Classifiers: SVMs and Naive Bayes
Description:You will learn about two more classification techniques in this module: first, Support Vector Machines (SVMs) and then Naive Bayes, a quick and highly interpretable approach that uses Bayes' theorem.
Name:Tree-Based Models, Ensemble Methods, and Evaluation
Description:In this module, you will first learn about classification using decision trees. We will see how to create models that use individual decision trees, and then ensemble models, which use many trees, such as bagging, boosting, and random forests. Then, we learn more about how to evaluate the performance of classifiers.
Name:Clustering Methods
Description:To this point, we have been focusing on supervised learning and training models that estimate a target variable that you have specified. In this module, we take our first look at unsupervised learning, a domain of machine learning that uses techniques to find patterns and relationships in data without you ever defining a target. In particular, we look at a variety of clustering techniques, beginning with k-means and hierarchical clustering, and then distribution and density-based clustering.
Name:Dimensionality Reduction and Temporal Models
Description:You will look at two new techniques in this module. The first is Principal Component Analysis, a powerful dimensionality reduction technique that you can use to project high-dimensional features into lower-dimensional spaces. This can be used for a range of purposes, including feature selection, preventing overfitting, visualizing in two- or three-dimensional spaces higher dimensional data, and more. Then, you will study hidden Markov models, a technique that you can use to model sequences of states, where each state depends on the one that came before.
Name:Deep Learning
Description:This module introduces you to one of the most hyped topics in machine learning, deep learning with feed-forward neural networks and convolutional neural networks. You will learn about how these techniques work and where they might be very effective--or very ineffective. We explore how to design, implement, and evaluate such models using Python and Keras.
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study.
