Certificate in AI Recommender Systems

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive Online e-Learning Access (LMS)
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification



$49.00 /-

AI Recommender Systems are algorithms and models designed to suggest or recommend items to users based on their preferences, behavior, or past interactions. These systems are commonly used in various online platforms, such as e-commerce websites, streaming services, social media platforms, and more, to provide personalized recommendations to users.

Note: Please note that the course comes with online e-learning (videos) only. No hard copy will be provided.

Why should one take AI Recommender Systems Certification?

Recommender systems use machine learning algorithms to predict user preferences and filter through vast amounts of data to suggest the most relevant items. By offering personalized recommendations, these systems aim to improve user engagement, satisfaction, and retention on platforms.

The Vskills AI Recommender Systems certification attests to your understanding and applying skills and knowledge in AI recommender systems and helps you to stand out amongst your peers to help in gaining better career prospects.

Who will benefit from taking AI Recommender Systems Certification?

Data scientists, machine learning engineers, software engineers and developers benefit immensely by opting for Vskills Certificate in AI Recommender Systems to gain an edge in the competitive job market.

Students taking the certification also gain by showcasing their understanding of AI Recommender Systems and are able to increase their job opportunities.

AI Recommender Systems Table of Contents

https://www.vskills.in/certification/ai-recommender-systems-certification-table-of-contents

AI Recommender Systems Practice Questions

https://www.vskills.in/practice/ai-recommender-systems-practice-questions

AI Recommender Systems Interview Questions

https://www.vskills.in/interview-questions/ai-recommender-systems-interview-questions

Companies that hire AI Recommender Systems Professionals

E-commerce platforms, streaming services, social media platforms, tech companies and startups are constantly hiring skilled AI recommender systems professionals. Companies employing AI Recommender Systems professionals include Google, TCS, Accenture, IBM, Tech Mahindra, GE, Amex, Deloitte, Wipro, TCS, etc.

AI Recommender Systems Related Blogs

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AI Recommender Systems Jobs

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AI Recommender Systems Internships

Vskills runs its flagship internship program where bright interns work with academic council, click to know more details..

Apply for AI Recommender Systems Professional Certification

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TABLE OF CONTENT

Introduction

  • AI Sciences
  • Course Outline
  • Machine Learning Recommender Systems
  • Deep Learning Recommender Systems

Recommender Systems with Machine Learning

  • Recommender Systems Overview
  • Introduction to Recommender Systems
  • Recommender Systems Process and Goals
  • Generations of Recommender Systems
  • Nexus of AI and Recommender Systems
  • Applications and Real-World Challenges
  • Quiz
  • Quiz Solution
  • Basics of Recommender System: Overview
  • Basics of Recommender System: Taxonomy of Recommender Systems
  • Basics of Recommender System: ICM
  • Basics of Recommender System: User Rating Matrix
  • Basics of Recommender System: Quality of Recommender System
  • Basics of Recommender System: Online Evaluation Techniques
  • Basics of Recommender System: Offline Evaluation Techniques
  • Basics of Recommender System: Data Partitioning
  • Basics of Recommender System: Important Parameters
  • Basics of Recommender System: Error Metric Computation
  • Basics of Recommender System: Content-Based Filtering
  • Basics of Recommender System: Collaborative Filtering and User-Based Collaborative Filtering
  • Basics of Recommender System: Item Model and Memory-Based Collaborative Filtering
  • Basics of Recommender System: Quiz
  • Basics of Recommender System: Quiz Solution
  • Machine Learning for Recommender Systems: Overview
  • Machine Learning for Recommender Systems: Benefits of Machine Learning
  • Machine Learning for Recommender Systems: Guidelines for ML
  • Machine Learning for Recommender Systems: Design Approaches for ML
  • Machine Learning for Recommender Systems: Content-Based Filtering
  • Machine Learning for Recommender Systems: Data Preparation for Content-Based Filtering
  • Machine Learning for Recommender Systems: Data Manipulation for Content-Based Filtering
  • Machine Learning for Recommender Systems: Exploring Genres in Content-Based Filtering
  • Machine Learning for Recommender Systems: tf-idf Matrix
  • Machine Learning for Recommender Systems: Recommendation Engine
  • Machine Learning for Recommender Systems: Making Recommendations
  • Machine Learning for Recommender Systems: Item-Based Collaborative Filtering
  • Machine Learning for Recommender Systems: Item-Based Filtering Data Preparation
  • Machine Learning for Recommender Systems: Age Distribution for Users
  • Machine Learning for Recommender Systems: Collaborative Filtering using KNN
  • Machine Learning for Recommender Systems: Geographic Filtering
  • Machine Learning for Recommender Systems: KNN Implementation
  • Machine Learning for Recommender Systems: Making Recommendations with Collaborative Filtering
  • Machine Learning for Recommender Systems: User-Based Collaborative Filtering
  • Machine Learning for Recommender Systems: Quiz
  • Machine Learning for Recommender Systems: Quiz Solution
  • Project 1: Song Recommendation System Using Content-Based Filtering: Project Introduction
  • Project 1: Song Recommendation System Using Content-Based Filtering: Dataset Usage
  • Project 1: Song Recommendation System Using Content-Based Filtering: Missing Values
  • Project 1: Song Recommendation System Using Content-Based Filtering: Exploring Genres
  • Project 1: Song Recommendation System Using Content-Based Filtering: Occurrence Count
  • Project 1: Song Recommendation System Using Content-Based Filtering: tf-idf Implementation
  • Project 1: Song Recommendation System Using Content-Based Filtering: Similarity Index
  • Project 1: Song Recommendation System Using Content-Based Filtering: Fuzzywuzzy Implementation
  • Project 1: Song Recommendation System Using Content-Based Filtering: Find st Title
  • Project 1: Song Recommendation System Using Content-Based Filtering: Making Recommendations
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Project Introduction
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Dataset Discussion
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Rating Plot
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Count
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Logarithm of Count
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Active Users and Popular Movies
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Create Collaborative Filter
  • Project 2: Movie Recommendation System Using Collaborative Filtering: KNN Implementation
  • Project 2: Movie Recommendation System Using Collaborative Filtering: Making Recommendations

Deep Learning for Recommender Systems: An Applied Approach

  • Deep Learning Foundation for Recommender Systems: Module Introduction
  • Deep Learning Foundation for Recommender Systems: Overview
  • Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation systems
  • Deep Learning Foundation for Recommender Systems: Inference After Training
  • Deep Learning Foundation for Recommender Systems: Inference Mechanism
  • Deep Learning Foundation for Recommender Systems: Embeddings and User Context
  • Deep Learning Foundation for Recommender Systems: Neural Collaborative Filtering
  • Deep Learning Foundation for Recommender Systems: VAE Collaborative Filtering
  • Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL Models
  • Deep Learning Foundation for Recommender Systems: Deep Learning Quiz
  • Deep Learning Foundation for Recommender Systems: Deep Learning Quiz Solution
  • Project Amazon Product Recommendation System: Module Overview
  • Project Amazon Product Recommendation System: TensorFlow Recommenders
  • Project Amazon Product Recommendation System: Two-Tower Model
  • Project Amazon Product Recommendation System: Project Overview
  • Project Amazon Product Recommendation System: Download Libraries
  • Project Amazon Product Recommendation System: Data Visualization with WordCloud
  • Project Amazon Product Recommendation System: Make Tensors from DataFrame
  • Project Amazon Product Recommendation System: Rating Our Data
  • Project Amazon Product Recommendation System: Random Train-Test Split
  • Project Amazon Product Recommendation System: Making the Model and Query Tower
  • Project Amazon Product Recommendation System: Candidate Tower and Retrieval System
  • Project Amazon Product Recommendation System: Compute Loss
  • Project Amazon Product Recommendation System: Train and Validation
  • Project Amazon Product Recommendation System: Accuracy Versus Recommendations
  • Project Amazon Product Recommendation System: Making Recommendations

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