Learn how to create content based hotel recommendations
Learn how to create content based hotel recommendations, available at $29.99, has an average rating of 4.25, with 12 lectures, based on 4 reviews, and has 29 subscribers.
You will learn about content based recommendations This course is ideal for individuals who are Beginner Python developers who are curious about recommendations It is particularly useful for Beginner Python developers who are curious about recommendations.
Enroll now: Learn how to create content based hotel recommendations
Summary
Title: Learn how to create content based hotel recommendations
Price: $29.99
Average Rating: 4.25
Number of Lectures: 12
Number of Published Lectures: 12
Number of Curriculum Items: 12
Number of Published Curriculum Objects: 12
Original Price: $22.99
Quality Status: approved
Status: Live
What You Will Learn
- content based recommendations
Who Should Attend
- Beginner Python developers who are curious about recommendations
Target Audiences
- Beginner Python developers who are curious about recommendations
Course Description
Learn to build a recommendation engine with Content-Based filtering
Build a strong foundation in Content-Based Recommendation Systems with this tutorial for beginners.
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Understanding of recommendation systems
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Types of recommendation systems
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Tokenization
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Stop words removal
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n-grams
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TF-IDF Vectorizer
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Cosine similarity algorithm
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User Jupyter Notebook for programming
A Powerful Skill at Your Fingertips Learning the fundamentals of a recommendation system puts a powerful and handy tool at your fingertips. Python and Jupyter are free, easy to learn, have excellent documentation.
Jobs in the recommendation systems area are plentiful, and learning content-based filtering will give you a strong edge. Content-based filtering has the advantage of recommending articles when you have a new app or site, and there are no users yet for the site.
Content-Based Recommendation Systems are becoming very popular. Amazon, Walmart, Google eCommerce websites are a few famous examples of recommendation systems in action. Recommendation Systems are vital in information retrieval, upselling, and cross-selling of products. Learning Collaborative filtering with SVD will help you become a recommendation system developer who is in high demand.
Big companies like Google, Facebook, Microsoft, Airbnb, and Linked In are already using recommendation systems with content-based recommendations in information retrieval and social platforms. They claimed that using recommendation systems has boosted the productivity of the entire company significantly.
Content and Overview
This course teaches you how to build recommendation systems using open-source Python and Jupyter framework. You will work along with me step by step to build the following answers.
Introduction to recommendation systems.
Introduction to Collaborative filtering
Build a jupyter notebook step by step using item-based collaborative filtering
Build a real-world web application to recommend music
What am I going to get from this course?
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Learn recommendation systems and build a real-world hotel recommendation engine from a professional trainer from your own desk.
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Over 10 lectures teaching you how to build real-world recommendation systems
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Suitable for beginner programmers and ideal for users who learn faster when shown.
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Visual training method, offering users increased retention and accelerated learning.
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Breaks even the most complex applications down into simplistic steps.
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Offers challenges to students to enable the reinforcement of concepts. Also, solutions are described to validate the challenges.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: About Author
Chapter 2: Set up
Lecture 1: Install Anaconda
Lecture 2: Python Packages
Chapter 3: Background on recommendations
Lecture 1: TF-IDF
Lecture 2: Recommendation alogirithm
Lecture 3: Design
Chapter 4: Code
Lecture 1: Understanding Hotel Data
Lecture 2: Tokenization and stop words removal
Chapter 5: TF IDF Vectorizer
Lecture 1: TFIDF Vectorizer and Cosine Similarity
Chapter 6: Hote Recommendations
Lecture 1: Hotel Recmmendation
Chapter 7: Final Thoughts
Lecture 1: Next Steps
Instructors
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Evergreen Technologies
Software Mentor
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 2 votes
- 5 stars: 1 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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