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.
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課程信息
8,653 次近期查看
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
Python ProgrammingBig Data ProductsRecommender Systems
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完成後獲得證書
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立即開始，按照自己的計劃學習。
第 4 門課程（共 4 門）
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完成時間大約為11 小時
英語（English）
字幕：英語（English）
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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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 2 小時
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
完成時間為 2 小時
5 個視頻 （總計 54 分鐘）, 3 個閱讀材料, 3 個測驗
5 個視頻
Recommender Systems versus Other Forms of Supervised Learning7分鐘
Collaborative Filtering-Based Recommendation19分鐘
Latent Factor Models (Part 1)11分鐘
Latent Factor Models (Part 2)11分鐘
3 個閱讀材料
Syllabus10分鐘
Course Materials10分鐘
Setting Up Your System10分鐘
3 個練習
Review: Recommender Systems4分鐘
Review: Introduction to Latent Factor Models4分鐘
Recommender Systems and Latent Factor Models20分鐘
完成時間為 1 小時
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.
完成時間為 1 小時
4 個視頻 （總計 36 分鐘）
4 個視頻
Similarity-Based Recommender for Rating Prediction7分鐘
Implementing a Latent Factor Model (Part 1)11分鐘
Implementing a Latent Factor Model (Part 2)6分鐘
3 個練習
Review: Similarity-Based Recommenders5分鐘
Review: Implementing Latent Factor Models4分鐘
Implementing Recommender Systems10分鐘
完成時間為 1 小時
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.
完成時間為 1 小時
3 個視頻 （總計 17 分鐘）, 1 個閱讀材料, 2 個測驗
1 個閱讀材料
Setting up Your Workspace with Docker: Django10分鐘
2 個練習
Review: Flask and Django30分鐘
Deploying Recommender Systems5分鐘
完成時間為 2 小時
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!
完成時間為 2 小時
2 個閱讀材料
2 個閱讀材料
Project Description10分鐘
How to Find a Dataset10分鐘
關於 Python Data Products for Predictive Analytics 專項課程
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 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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