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.
Working in a hands-on lab environment led by our expert instructor, attendees will
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 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
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:
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
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
Manipulating Data with the Pandas Library
Building an IMDB Top 250 Clone with Pandas
Building Content-Based Recommenders
Getting Started with Data Mining Techniques
Building Collaborative Filters
Hybrid Recommenders
Student Materials: Each participant will receive a digital Student Guide and/or Course Notes, code samples, software tutorials, step-by-step written lab instructions (as applicable), diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.
Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment, or local installation, is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. In some cases we can also help you install this course locally if preferred. Please inquire for details and options.
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Please see the current upcoming available open enrollment course dates posted below. Please feel free to Register Online below, or call 844-475-4559 toll free to connect with our Registrar for assistance. If you need additional date options, please contact us for scheduling.
Course Title | Days | Date | Time | Price | |
---|---|---|---|---|---|
Building Recommendation Systems with Python (TTAML012) | 3 Days | Jul 12 to Jul 14 | 10:00 AM to 06:00 PM EST | $2,295.00 | Enroll |
Building Recommendation Systems with Python (TTAML012) | 3 Days | Sep 27 to Sep 29 | 10:00 AM to 06:00 PM EST | $2,295.00 | Enroll |
Building Recommendation Systems with Python (TTAML012) | 3 Days | Nov 29 to Dec 1 | 10:00 AM to 06:00 PM EST | $2,295.00 | Enroll |
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