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TTAML0002 Building Recommendation Systems With Python Training

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Recommendation Systems with Python provide good recommendations regarding groceries, friends, and movies, enticing customers to use their platform and define the user experience.

  • Category : Python

Course Price : $1999 Per Participant

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Course Description

In almost every internet business today, professionals will find recommendation systems.

Recommendation Systems with Python provide good recommendations regarding groceries, friends, and movies, enticing customers to use their platform and define the user experience.

This course will make professionals learn about various recommenders used in the industry and from scratch using Python to build them.

Here, professionals will build a content-based engine, an IMDB Top 250 clone, which works on movie metadata.

Professionals will also learn collaborative filtering techniques in this course, helping them grow their careers.

By learning this program, professionals will also learn the extensive functionality of the Python module.

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What you will learn

  • green-tick Understand the different kinds of recommender systems
  • green-tick Master data-wrangling techniques using the pandas library
  • green-tick Building an IMDB Top 250 Clone
  • green-tick Build a content-based engine to recommend movies based on real movie metadata
  • green-tick Employ data-mining techniques used in building recommenders
  • green-tick Build industry-standard collaborative filters using powerful algorithms
  • green-tick Building Hybrid Recommenders that incorporate content based and collaborative filtering

    • This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work. This is not a basic class.

Prerequisites

  • 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 us

Recommended

Who should attend this course?

Developers, Analysts, and other professionals interested in learning the tools and techniques needed to build recommendation systems.

Schedules

Oops! For this course, there are currently no public schedules available. Clicking on "Notify Me" will allow you to express your interest.

For dates, times, and location customization of this course, get in touch with us.

You can also speak with a learning consultant by calling 800-961-0337.

Curriculum

  • Technical requirements
  • What is a recommender system?
  • Types of recommender systems
  • Technical requirements
  • Setting up the environment
  • The Pandas library
  • The Pandas DataFrame
  • The Pandas Series
  • Technical requirements
  • The simple recommender
  • The knowledge-based recommender
  • Technical requirements
  • Exporting the clean DataFrame
  • Document vectors
  • The cosine similarity score
  • Plot description-based recommender
  • Metadata-based recommender
  • Suggestions for improvements
  • Problem statement
  • Similarity measures
  • Clustering
  • Dimensionality reduction
  • Supervised learning
  • Evaluation metrics
  • Technical requirements
  • The framework
  • User-based collaborative filtering
  • Item-based collaborative filtering
  • Model-based approaches
  • Technical requirements
  • Introduction
  • Case study and final project – Building a hybrid model

    • Course Details

    • skill skill-green
      Skill Level: Intermediate
    • enroll enroll-green
      Enrolled: 1422
    • duration duration green
      Duration: 3 Days
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