In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.
Este curso faz parte do Programa de cursos integrados Python Data Products for Predictive Analytics
oferecido por
Programa de cursos integrados Python Data Products for Predictive AnalyticsUniversidade da Califórnia, San DiegoInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 11 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
O que você vai aprender
Project structure of interactive Python data applications
Python web server frameworks: (e.g.) Flask, Django, Dash
Best practices around deploying ML models and monitoring performance
Deployment scripts, serializing models, APIs
Habilidades que você terá
Python ProgrammingBig Data ProductsRecommender Systems
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 11 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
Universidade da Califórnia, 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.
Programa - O que você aprenderá com este curso
3 horas para concluir
Introduction
Welcome to the first week of Deploying Machine Learning Models! 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 recommender systems and differentiate it from other types of machine learning
3 horas para concluir
5 vídeos (Total 54 mín.), 3 leituras, 3 testes
5 videos
Recommender Systems versus Other Forms of Supervised Learning7min
Collaborative Filtering-Based Recommendation19min
Latent Factor Models (Part 1)11min
Latent Factor Models (Part 2)11min
3 leituras
Syllabus10min
Course Materials10min
Setting Up Your System10min
3 exercícios práticos
Review: Recommender Systems30min
Review: Introduction to Latent Factor Models30min
Recommender Systems and Latent Factor Models20min
1 hora para concluir
Implementing Recommender Systems
This week, we will learn how to implement a similarity-based recommender, returning predictions similar to an user's given item. We will cover how to optimize these models based on gradient descent and Jaccard similarity.
1 hora para concluir
4 vídeos (Total 36 mín.)
4 videos
Similarity-Based Recommender for Rating Prediction7min
Implementing a Latent Factor Model (Part 1)11min
Implementing a Latent Factor Model (Part 2)6min
3 exercícios práticos
Review: Similarity-Based Recommenders5min
Review: Implementing Latent Factor Models4min
Implementing Recommender Systems10min
1 hora para concluir
Deploying Recommender Systems
This week, we will learn about Python web server frameworks and the overall structure of interactive Python data applications. We will also cover some tips for best practices on deploying and monitoring your applications.
1 hora para concluir
3 vídeos (Total 17 mín.), 1 leitura, 2 testes
1 leituras
Setting up Your Workspace with Docker: Django10min
2 exercícios práticos
Review: Flask and Django30min
Deploying Recommender Systems5min
2 horas para concluir
Project 4: Recommender System
For this final project, you will build a recommender system of your own. Find a dataset, clean it, and create a predictive system from the dataset. This will help prepare you for the upcoming capstone, where you will harness your skills from all courses of this specialization into one single project!
2 horas para concluir
2 leituras
2 leituras
Project Description10min
How to Find a Dataset10min
Avaliações
Principais avaliações do DEPLOYING MACHINE LEARNING MODELS
por ML6 de Dez de 2020
I Liked the Course in general especially the recommender component. I would seriously recommend making major improvements and clarification to the capstone project.
Sobre Programa de cursos integrados 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.
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