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.
Ce cours fait partie de la Spécialisation Python Data Products for Predictive Analytics
Meaningful Predictive Modeling
proposé par
À propos de ce cours
4,406 consultations récentes
Ce que vous allez apprendre
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.
Les étudiants prenant part à ce Course sont
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Data Analysts
- Consultants
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
12 heures pour terminer
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 vidéos (Total 49 min), 4 lectures, 3 quiz
6 vidéos
Motivation Behind the MSE8 min
Regression Diagnostics: MSE and R²6 min
Over- and Under-Fitting6 min
Classification Diagnostics: Accuracy and Error11 min
Classification Diagnostics: Precision and Recall12 min
4 lectures
Syllabus10 min
Setting Up Your System10 min
(Optional) Additional Resources and Recommended Readings10 min
Course Materials10 min
3 exercices pour s'entraîner
Review: Regression Diagnostics8 min
Review: Classification Diagnostics4 min
Diagnostics for Data30 min
Semaine
22 heures pour terminer
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 vidéos (Total 35 min), 4 quiz
4 vidéos
Model Complexity and Regularization10 min
Adding a Regularizer to our Model, and Evaluating the Regularized Model8 min
Evaluating Classifiers for Ranking4 min
4 exercices pour s'entraîner
Review: Setting Up a Codebase2 min
Review: Regularization5 min
Review: Evaluating a Model5 min
Codebases, Regularization, and Evaluating a Model45 min
Semaine
31 heure pour terminer
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 vidéos (Total 24 min), 3 quiz
4 vidéos
Validation5 min
“Theorems” About Training, Testing, and Validation8 min
Implementing a Regularization Pipeline in Python5 min
Guidelines on the Implementation of Predictive Pipelines5 min
3 exercices pour s'entraîner
Review: Validation4 min
Review: Predictive Pipelines6 min
Predictive Pipelines20 min
Semaine
42 heures pour terminer
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 lectures, 1 quiz
2 lectures
Project Description10 min
Where to Find Datasets10 min
Enseignants
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
À propos de Université de Californie à San Diego
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.
À propos du Spécialisation Python Data Products for Predictive Analytics
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 and deploy machine learning models 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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