This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.
本课程是 Python Data Products for Predictive Analytics 专项课程 专项课程的一部分
Meaningful Predictive Modeling
提供方
课程信息
您将学到的内容有
Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).
Evaluate the performance of regressors / classifiers using the above measures.
Understand the difference between training/testing performance, and generalizability.
Understand techniques to avoid overfitting and achieve good generalization performance.
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 2 小时
Week 1: Diagnostics for Data
For this first 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 diagnostics for the results of supervised learning.
6 个视频 （总计 49 分钟）, 4 个阅读材料, 3 个测验
6 个视频
Motivation Behind the MSE8分钟
Regression Diagnostics: MSE and R²6分钟
Over- and Under-Fitting6分钟
Classification Diagnostics: Accuracy and Error11分钟
Classification Diagnostics: Precision and Recall12分钟
4 个阅读材料
Syllabus10分钟
Setting Up Your System10分钟
(Optional) Additional Resources10分钟
Course Materials10分钟
3 个练习
Review: Regression Diagnostics8分钟
Review: Classification Diagnostics4分钟
Diagnostics for Data30分钟
周
2完成时间为 1 小时
Week 2: Codebases, Regularization, and Evaluating a Model
This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.
4 个视频 （总计 35 分钟）, 4 个测验
4 个视频
Model Complexity and Regularization10分钟
Adding a Regularizer to our Model, and Evaluating the Regularized Model8分钟
Evaluating Classifiers for Ranking4分钟
4 个练习
Review: Setting Up a Codebase2分钟
Review: Regularization5分钟
Review: Evaluating a Model5分钟
Codebases, Regularization, and Evaluating a Model30分钟
周
3完成时间为 1 小时
Week 3: Validation and Pipelines
This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.
4 个视频 （总计 24 分钟）, 3 个测验
4 个视频
Validation5分钟
“Theorems” About Training, Testing, and Validation8分钟
Implementing a Regularization Pipeline in Python5分钟
Guidelines on the Implementation of Predictive Pipelines5分钟
3 个练习
Review: Validation4分钟
Review: Predictive Pipelines6分钟
Predictive Pipelines20分钟
周
4完成时间为 2 小时
Final Project
In the final week of this course, you will continue building on the project from the first and second courses 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. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.
2 个阅读材料, 1 个测验
2 个阅读材料
Project Description10分钟
Where to Find Datasets10分钟
讲师
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
关于 加州大学圣地亚哥分校
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
关于 Python Data Products for Predictive Analytics 专项课程
Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems with this four-course Specialization from UC San Diego.
This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills.
Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets.
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