使用Python的推荐系统真实世界项目 Recommendation system Real World Projects using Python

数据科学、机器学习和人工智能技术推荐系统的真实世界项目..

你会学到什么
了解如何解决现实世界的问题..
学习基于协作的过滤
了解如何使用相关性来推荐类似的电影或类似的书籍
基于学习内容的推荐系统
了解如何使用不同的技术，如平均加权，混合模型等..
学习不同类型的推荐系统

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥CG资源站 机译）|大小解压后:2.22 GB |时长:4h 27m

要求
对于前面的部分，只需了解一些基本的算术
精通Python..
描述
信不信由你，如今几乎所有的在线平台都以某种方式使用推荐系统。

那么“推荐系统”代表什么，为什么它们如此有用？

让我们看看互联网上排名前三的网站:谷歌、YouTube和Netfix

谷歌:搜索结果

这就是为什么谷歌是当今最成功的科技公司。

YouTube:视频仪表板

当我有更重要的事情要做的时候，我肯定不是唯一一个不小心在YouTube上花了几个小时的人！他们是怎么说服你这么做的？

没错，这都是因为推荐系统！

网飞:在根据用户的行为向用户推荐合适的电影方面如此强大！

推荐系统旨在预测用户的兴趣，并推荐他们很可能感兴趣的产品。

本课程让您全面了解推荐系统。

在本课程中，我们将涵盖

推荐系统的用例。

平均加权技术推荐系统

基于流行度的推荐系统

基于平均加权和流行度的混合模型

协同过滤。

基于内容的过滤

还有更多！

这门课是给谁的
数据科学家
数据分析师
机器学习工程师
任何想深入研究数据科学的人。
想要获得实践的学生和专业人士..


MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.07 GB | Duration: 4h 27m

Real World Projects on recommendation systems with data science, machine learning and AI techniques..

What you’ll learn
Learn How to tackle Real world Problems..
Learn Collaborative based filtering
Learn how to use Correlation for Recommending similar Movies or similar books
Learn Content based recommendation system
Learn how to use different Techniques like Average Weighted , Hybrid Model etc..
Learn different types of Recommender Systems

Requirements
For earlier sections, just know some basic arithmetic
Be proficient in Python ..
Description
Believe it or not, almost all online platforms today uses recommender systems in some way or another.

So What does “recommender systems” stand for and why are they so useful?

Let’s look at the top 3 websites on the Internet : Google, YouTube, and Netfix

Google: Search results

Thats why Google is the most successful technology company today.

YouTube: Video dashboard

I’m sure I’m not the only one who’s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

That’s right this is all on account of Recommender systems!

Netflix: So powerful in terms of recommending right movies to users according to the behaviour of users !

Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them.

This course gives you a thorough understanding of the Recommendation systems.

In this course, we will cover

Use cases of recommender systems.

Average weighted Technique Recommender System

Popularity-based Recommender System

Hybrid Model based on Average weighted & Popularity

Collaborative filtering.

Content based filtering

and much, much more!

Not only this, you will also work on two very exciting projects.

Instructor Support – Quick Instructor Support for any query within 2-3 hours

All the resources used in this course will be shared with you via Google Drive Link

How to make most from the course ?

Check out the lecture “Utilize This Golden Oppurtunity , QnA Section !”

Who this course is for
Data Scientists
Data Analysts
Machine learning Engineer
Anyone who wants to deep dive into data science.
Students and Professionals who want to gain Hands-on..
云桥CG资源站 为三维动画制作，游戏开发员、影视特效师等CG艺术家提供视频教程素材资源！