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.This skills-focused ccombines 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.
- Price: $2,195.00
- Duration: 1 day
- Delivery Methods: Virtual
| Date | Time | Price | Option |
|---|---|---|---|
| Please contact us at info@toptalentlearning.com or 469-721-6100 for this course schedule. |
For questions call: (469) 721-6100
Why choose
TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.
Getting Started with Recommender Systems
- Technical requirements
- What is a recommender system?
- Types of recommender systems
Manipulating Data with the Pandas Library
- Technical requirements
- Setting up the environment
- The Pandas library
- The Pandas DataFrame
- The Pandas Series
Building an IMDB Top 250 Clone with Pandas
- Technical requirements
- The simple recommender
- The knowledge-based recommender
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
Getting Started with Data Mining Techniques
- Problem statement
- Similarity measures
- Clustering
- Dimensionality reduction
- Supervised learning
- Evaluation metrics
Building Collaborative Filters
- Technical requirements
- The framework
- User-based collaborative filtering
- Item-based collaborative filtering
- Model-based approaches
Hybrid Recommenders
- Technical requirements
- Introduction
- Case study and final project – Building a hybrid model
This skills-focused ccombines 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
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web.
Attending students should have the following incoming skills:
- Basic to Intermediate IT Skills.
- Basic Python syntax skills are recommended. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.
- Good foundational mathematics or logic skills
- Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
