Unleash the power of AI with our course designed for IT professionals ready to enhance user engagement through sophisticated recommendation systems using Python.
EnrollIdentify various recommendation system types.
Utilize Pandas for effective data preparation.
Develop advanced content-based recommender solutions.
Implement collaborative filtering methods.
In the digital era, personalized experiences are key to user engagement. Our comprehensive two-day course delves into constructing advanced recommendation systems using Python. You'll explore the essential concepts, engage in hands-on labs with Pandas, and learn to create recommenders based on content and collaboration filters. The course wraps up as you deploy a practical recommender system using Docker, preparing you to enhance digital platforms with tailored user experiences.
Core concepts of recommendation
Exploring system types
Interactive lab session
Environment configuration
Introduction to Pandas library
Utilizing Pandas DataFrame and Series
Lab activities
Fundamental techniques overview
Knowledge-based recommender systems
Practical lab session
Data preparation strategies
Implementing document vectors
Calculating cosine similarity
Leveraging metadata and suggestions
Hands-on activity
Exploring problem-solving strategies
Evaluating similarity measures
Utilizing clustering and dimensionality reduction
Supervised learning overview
Interactive lab session
Architecture and foundational methods
User-driven and item-driven strategies
Model-based filtering approaches
Practical lab activity
API packaging processes
Docker integration steps
Real-world deployment exercises
Final lab session
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier