Recommendation system Real World Projects using Python
Real-world projects that use data science, machine learning, and AI to make recommendations.
What you’ll learn
Recommendation system Real World Projects using Python
- Learn how to solve problems in the real world.
- Take a look at collaborative filtering.
- Learn how to use Correlation to recommend movies that are similar or books that are similar.
- Take a look at a content-based recommendation system.
- Learn how to use different techniques, like the Average Weighted Model, the Hybrid Model, and so on.
- Learn about the different types of Recommender Systems that are out there.
Requirements
- For the earlier parts, just know some basic math.
- Python is a very good language.
Description
Believe it or not, almost all of the websites and apps you use today use some kind of recommender system in some way or another.
A recommendation system is a system that tells you what to buy and how to buy it.
Our first stop is to look at Google, YouTube, and Netflix.
Google: The results of a search on their site
The reason Google is the best technology company today is that it has a lot of good ideas.
A video dashboard is called YouTube, and it is on the site.
How do they make you do that?
That’s right, Recommender systems are to blame for this.
Users’ behaviour can help Netflix
Recommender systems try to figure out what people like and then show them products that might be interesting to them.
There is a lot to learn about the Recommendation systems in this class.
We will cover:
- There are many examples of how recommender systems can be used in real life.
- Weighted Technique Recommender System that is averaged.
- Recommendation system based on popularity.
- An average-weighted and popular model is used in this hybrid model.
- Collaboratively filtering out.
- Useful content-based filtering.
- … and a lot more!
You’ll also work on two very exciting projects.
Who this course is for:
- This group of people is called “data scientists.”
- People who work with data.
- An engineer in machine learning.
- Anyone who wants to get more in-depth into data science should take this course.
- College students and professionals who want to learn how to do things on their own
