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.
Ce cours fait partie de la Spécialisation Python Data Products for Predictive Analytics
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Spécialisation Python Data Products for Predictive AnalyticsUniversité de Californie à San DiegoÀ propos de ce cours
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Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
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Approx. 11 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Python ProgrammingBig Data ProductsRecommender Systems
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 11 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
5 vidéos (Total 54 min), 3 lectures, 3 quiz
5 vidéos
Recommender Systems versus Other Forms of Supervised Learning7 min
Collaborative Filtering-Based Recommendation19 min
Latent Factor Models (Part 1)11 min
Latent Factor Models (Part 2)11 min
3 lectures
Syllabus10 min
Course Materials10 min
Setting Up Your System10 min
3 exercices pour s'entraîner
Review: Recommender Systems30 min
Review: Introduction to Latent Factor Models30 min
Recommender Systems and Latent Factor Models20 min
1 heure pour terminer
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 heure pour terminer
4 vidéos (Total 36 min)
4 vidéos
Similarity-Based Recommender for Rating Prediction7 min
Implementing a Latent Factor Model (Part 1)11 min
Implementing a Latent Factor Model (Part 2)6 min
3 exercices pour s'entraîner
Review: Similarity-Based Recommenders5 min
Review: Implementing Latent Factor Models4 min
Implementing Recommender Systems10 min
1 heure pour terminer
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 heure pour terminer
3 vidéos (Total 17 min), 1 lecture, 2 quiz
1 lecture
Setting up Your Workspace with Docker: Django10 min
2 exercices pour s'entraîner
Review: Flask and Django30 min
Deploying Recommender Systems5 min
2 heures pour terminer
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 heures pour terminer
2 lectures
2 lectures
Project Description10 min
How to Find a Dataset10 min
Avis
Meilleurs avis pour DEPLOYING MACHINE LEARNING MODELS
par ML6 déc. 2020
I Liked the Course in general especially the recommender component. I would seriously recommend making major improvements and clarification to the capstone project.
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.
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