AI-Powered Personalization: Creating Recommender Systems with Python

Unleash the power of AI with our course designed for IT professionals ready to enhance user engagement through sophisticated recommendation systems using Python.

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Essential Skills Gained

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Identify various recommendation system types.

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Utilize Pandas for effective data preparation.

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Develop advanced content-based recommender solutions.

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Implement collaborative filtering methods.

Format

  • Instructor-led
  • 3 days with lectures and hands-on labs.

Audience

  • Experienced web developers
  • Data analysis professionals
  • Machine learning specialists
  • Digital product strategists

Description

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.

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Upcoming Course Dates

September 18-19, 2025

10:00 AM - 6:00 PM

Virtual: Online - US/Eastern

Enroll

$2295

November 17-18, 2025

10:00 AM - 6:00 PM

Virtual: Online - US/Eastern

Enroll

$2295

Course Outline

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Introduction to Recommender Systems

  1. Core concepts of recommendation

  2. Exploring system types

  3. Interactive lab session

Data Manipulation with Pandas

  1. Environment configuration

  2. Introduction to Pandas library

  3. Utilizing Pandas DataFrame and Series

  4. Lab activities

Initial Recommender Creation

  1. Fundamental techniques overview

  2. Knowledge-based recommender systems

  3. Practical lab session

Developing Content-Based Systems

  1. Data preparation strategies

  2. Implementing document vectors

  3. Calculating cosine similarity

  4. Leveraging metadata and suggestions

  5. Hands-on activity

Advanced Data Mining Techniques

  1. Exploring problem-solving strategies

  2. Evaluating similarity measures

  3. Utilizing clustering and dimensionality reduction

  4. Supervised learning overview

  5. Interactive lab session

Collaborative Filtering Techniques

  1. Architecture and foundational methods

  2. User-driven and item-driven strategies

  3. Model-based filtering approaches

  4. Practical lab activity

Recommender System Deployment

  1. API packaging processes

  2. Docker integration steps

  3. Real-world deployment exercises

  4. Final lab session

Your Team has Unique Training Needs.

Your team deserves training as unique as they are.

Let us tailor the course to your needs at no extra cost.