このコースについて
3,649 最近の表示

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約17時間で修了

推奨:9 hours/week...

英語

字幕:英語

学習内容

  • Check

    Project structure of interactive Python data applications

  • Check

    Python web server frameworks: (e.g.) Flask, Django, Dash

  • Check

    Best practices around deploying ML models and monitoring performance

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    Deployment scripts, serializing models, APIs

習得するスキル

Python ProgrammingBig Data ProductsRecommender Systems

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約17時間で修了

推奨:9 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
1時間で修了

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 readings, 3 quizzes
3件の学習用教材
Syllabus10 分
Course Materials10 分
Setting Up Your System10 分
3の練習問題
Review: Recommender Systems4 分
Review: Introduction to Latent Factor Models4 分
Recommender Systems and Latent Factor Models20 分
2
19分で修了

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....
3 quizzes
3の練習問題
Review: Similarity-Based Recommenders5 分
Review: Implementing Latent Factor Models4 分
Implementing Recommender Systems10 分
3
5分で修了

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 quiz
1の練習問題
Deploying Recommender Systems5 分
4
2時間で修了

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 readings, 1 quiz
2件の学習用教材
Project Description10 分
How to Find a Dataset10 分

講師

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Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center
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Julian McAuley

Assistant Professor
Computer Science

カリフォルニア大学サンディエゴ校について

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....

Python Data Products for Predictive Analyticsの専門講座について

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....
Python Data Products for Predictive Analytics

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