Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Recommendation Systems with Python

  • Course Code: Data Science - Recommendation Systems with Python
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: 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.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Recommendation Systems with Python for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: 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. 
  • Hands-on Learning: This 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. Student machines are required. 
  • Delivery Format: Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it’s 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’ll 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’ll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques 

Working in a hands-on learning environment, led by our Recommendation Systems expert instructor, students will learn about and explore: 

  • Build industry-standard recommender systems 
  • Only familiarity with Python is required 
  • No need to wade through complicated machine learning theory to use this course. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get to grips with 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 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 fltering 

Audience & Pre-Requisites 

This course is geared for attendees with Recommendation Systems with Python skills who wish to build recommendation systems is a familiarity with Python, and by the time you’re finished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 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 

Course Agenda / Topics 

  1. Getting Started with Recommender Systems 
  • Technical requirements 
  • What is a recommender system? 
  • Types of recommender systems 
  1. Manipulating Data with the Pandas Library 
  • Technical requirements 
  • Setting up the environment 
  • The Pandas library 
  • The Pandas DataFrame 
  • The Pandas Series 
  1. Building an IMDB Top 250 Clone with Pandas 
  • Technical requirements 
  • The simple recommender 
  • The knowledge-based recommender 
  1. 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 
  1. Getting Started with Data Mining Techniques 
  • Problem statement 
  • Similarity measures 
  • Clustering 
  • Dimensionality reduction 
  • Supervised learning 
  • Evaluation metrics 
  1. Building Collaborative Filters 
  • Technical requirements 
  • The framework 
  • User-based collaborative filtering 
  • Item-based collaborative filtering 
  • Model-based approaches 
  1. Hybrid Recommenders 
  • Technical requirements 
  • Introduction 
  • Case study – Building a hybrid model 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?