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TTAML0002 Building Recommendation Systems With Python Training

Category

Python

Rating
4.1
(4.1)
Price

$2295

Course Description

In almost every internet business today, professionals will find recommendation systems. Recommendation Systems with Python provides good recommendations regarding groceries, friends, and movies, enticing customers to use their platform and to 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.

Prerequisites for this training

  • 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

  • Python For Data Science Primer

Who should attend this course?

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

Schedules


Aug 11, 2021
10:00 am - 6:00 pm EST
Online

Oct 13, 2021
10:00 am - 6:00 pm EST
Online

Dec 08, 2021
10:00 am - 6:00 pm EST
Online
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What you will learn

  • Understand 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 real 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 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.

With Microtek Learning, you’ll receive:

  • Certified Instructor-led training
  • Industry Best Trainers
  • Official Training Course Student Handbook
  • Pre and Post assessments/evaluations
  • Collaboration with classmates (not available for a self-paced course)
  • Real-world knowledge activities and scenarios
  • Exam scheduling support*
  • Learn and earn program*
  • Practice Tests
  • Knowledge acquisition and exam-oriented
  • Interactive online course.
  • Support from an approved ITIL expert
  • For Government and Private pricing*
*For more details call: +1-800-961-0337 or email: info@microteklearning.com

Our Clients

For many years, Microtek Learning has been helping organizations, leaders, and professionals to reach their maximum performance by addressing the challenges they are facing.

  • 300+ enterprise clients
  • 100,000+ professionals trained
  • Service 70 of the Fortune 100
  • 96% of our clients would recommend us
our clients

Our Awards

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Why Choose Us?

Best price

Best Price in the Industry

You give better value in the marketplace. We will beat it.

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Trusted & Approved

Our courses are accredited by the authorized vendor.

delivery-methods

Many Delivery Methods

Flexible delivery methods are available here.

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High-Quality Resources

Resources are included for a comprehensive learning experience.

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
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    Course Details

    • Start Date: Aug 11, 2021
    • Duration: 3 Days
    • Skill Level: Intermediate
    • Certification: NO
    • Enrolled: 1422
    • Price: $2295
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