Course description
Building Recommendation Systems With Python
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.
This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.
Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.
Learning Objectives
This skills-focused course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project.
Working in a hands-on lab environment led by our expert instructor, attendees will
- Understand the different kinds of recommender systems
- Master data-wrangling techniques using the pandas library
- Building an IMDB Top 250 Clone
- Build a content-based engine to recommend movies based on real movie metadata
- Employ data-mining techniques used in building recommenders
- Build industry-standard collaborative filters using powerful algorithms
- Building Hybrid Recommenders that incorporate content based and collaborative filtering
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Training content
1. Getting Started with Recommender Systems
- Technical requirements
- What is a recommender system?
- Types of recommender systems
2. Manipulating Data with the Pandas Library
- Technical requirements
- Setting up the environment
- The Pandas library
- The Pandas DataFrame
- The Pandas Series
3. Building an IMDB Top 250 Clone with Pandas
- Technical requirements
- The simple recommender
- The knowledge-based recommender
4. Building Content-Based Recommenders
- Technical requirements
- Exporting the clean DataFrame
- Document vectors
- The cosine similarity score
- Plot description-based recommender
- Metadata-based recommender
- Suggestions for improvements
5. Getting Started with Data Mining Techniques
- Problem statement
- Similarity measures
- Clustering
- Dimensionality reduction
- Supervised learning
- Evaluation metrics
6. Building Collaborative Filters
- Technical requirements
- The framework
- User-based collaborative filtering
- Item-based collaborative filtering
- Model-based approaches
7. Hybrid Recommenders
- Technical requirements
- Introduction
- Case study and final project – Building a hybrid model
Course delivery details
Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files, datasets, code and solutions (as applicable) required for the hands-on work.
Classroom Setup Made Simple: Our dedicated tech team will work with you to ensure your classroom and lab environment is setup, tested and ready to go well in advance of the course delivery date, ensuring a smooth start to class and seamless hands-on experience for your students. We offer several flexible student machine setup options including guided manual set up for simple installation directly on student machines, or cloud based / remote hosted lab solutions where students can log in to a complete separate lab environment minus any installations, or we can supply complete turn-key, pre-loaded equipment to bring ready-to-go student machines to your facility. Please inquire for details.
Costs
- Price: $2,395.00
- Discounted Price: $1,556.75
Why choose Trivera Technologies LLC?
Over 25 years of technology training expertise.
Robust portfolio of over 1,000 leading edge technology courses.
Guaranteed to run courses and flexible learning options.
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Trivera Technologies
Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...