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. This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
本課程是 Python Data Products for Predictive Analytics 專項課程 專項課程的一部分
Deploying Machine Learning Models
提供方
課程信息
您將學到的內容有
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
您將獲得的技能
100% 在線
第 4 門課程（共 4 門）
可靈活調整截止日期
完成時間大約為11 小時
英語（English）
教學大綱 - 您將從這門課程中學到什麼
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
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.
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.
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!
講師
Ilkay Altintas
Chief Data Science Officer
Julian McAuley
Assistant Professor關於 加州大学圣地亚哥分校
關於 Python Data Products for Predictive Analytics 專項課程
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