Recommender Systems with Machine Learning

Recommender Systems with Machine Learning

by AI Sciences
Released March 2023
Publisher(s): Packt Publishing
ISBN: 9781837631667

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Video description

Have you ever thought how YouTube adjusts your feed as per your favorite content?

Ever wondered! Why is your Netflix recommending your favorite TV shows?

Have you ever wanted to build a customized recommender system for yourself?

Then this is the course you are looking for.

We will begin with the theoretical concepts and fundamental knowledge of recommender systems. You will gain an understanding of the essential taxonomies that form the foundation of these systems. You will be learning how to use the power of Python to evaluate your recommender systems datasets based on user ratings, user choices, music genres, categories of movies, and their year of release. A practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems.

Moving ahead, you will learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning models. Moreover, various projects have been included in this course to develop a very useful experience for you.

By the end of this course, you will be able to relate the concepts and theories for recommender systems in various domains, implement machine learning models for building real-world recommendation systems, and evaluate the machine learning models.

What Yoy Will Learn

  • Explore AI-integrated recommender systems basics
  • Look at the basic taxonomy of recommender systems
  • Study the impact of overfitting, underfitting, bias, and variance
  • Build content-based recommender systems with ML and Python
  • Build item-based recommender systems using ML techniques and Python
  • Learn to model KNN-based recommender engine for applications

Audience

No prior knowledge of recommender systems, machine learning, data analysis, or mathematics is needed. Only the working knowledge of basics of Python is required. You will start from the basics and gradually build your knowledge in the subject.

This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs.

The course is suitable for individuals who want to advance their skills in ML, master the relation of data analysis with ML, build customized recommender systems for their applications, and implement ML algorithms for recommender systems.

About The Author

AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.

Table of contents

  1. Chapter 1 : Introduction
    1. AI Sciences Introduction
    2. Instructor Introduction
    3. Overview of Recommender Systems
    4. Fundamentals of Recommender Systems
    5. Project Overview
  2. Chapter 2 : Motivation for Recommender System
    1. Recommender Systems Overview
    2. Introduction to Recommender Systems
    3. Recommender Systems Process and Goals
    4. Generations of Recommender Systems
    5. Nexus of AI and Recommender Systems
    6. Applications and Real-World Challenges
    7. Quiz
    8. Quiz Solution
  3. Chapter 3 : Basic of Recommender Systems
    1. Section Overview
    2. Taxonomy of Recommender Systems
    3. ICM
    4. User Rating Matrix
    5. Quality of Recommender Systems
    6. Online Evaluation Techniques
    7. Offline Evaluation Techniques
    8. Data Partitioning
    9. Important Parameters
    10. Error Metric Computation
    11. Content-Based Filtering
    12. Collaborative Filtering and User-Based Collaborative Filtering
    13. Item Model and Memory-Based Collaborative Filtering
    14. Quiz
    15. Quiz Solution
  4. Chapter 4 : Machine Learning for Recommender System
    1. Overview
    2. Benefits of Machine Learning
    3. Guidelines for ML
    4. Design Approaches for ML
    5. Content-Based Filtering
    6. Data Preparation for Content-Based Filtering
    7. Data Manipulation for Content-Based Filtering
    8. Exploring Genres in Content-Based Filtering
    9. tf-idf (Term Frequency-Inverse Document Frequency) Matrix
    10. Recommendation Engine
    11. Making Recommendations
    12. Item-Based Collaborative Filtering
    13. Item-Based Filtering Data Preparation
    14. Age Distribution for Users
    15. Collaborative Filtering Using KNN
    16. Geographic Filtering
    17. KNN Implementation
    18. Making Recommendations with Collaborative Filtering
    19. User-Based Collaborative Filtering
    20. Quiz
    21. Quiz Solution
  5. Chapter 5 : Project 1: Song Recommendation System Using Content-Based Filtering
    1. Project Introduction
    2. Dataset Usage
    3. Missing Values
    4. Exploring Genres
    5. Occurrence Count
    6. tf-idf (Term Frequency-Inverse Document Frequency) Implementation
    7. Similarity Index
    8. FuzzyWuzzy Implementation
    9. Find the Closest Title
    10. Making Recommendations
  6. Chapter 6 : Project 2: Movie Recommendation System Using Collaborative Filtering
    1. Project Introduction
    2. Dataset Discussion
    3. Rating Plot
    4. Count
    5. Logarithm of Count
    6. Active Users and Popular Movies
    7. Create Collaborative Filter
    8. KNN Implementation
    9. Making Recommendations
    10. Course Conclusion

Product information

  • Title: Recommender Systems with Machine Learning
  • Author(s): AI Sciences
  • Release date: March 2023
  • Publisher(s): Packt Publishing
  • ISBN: 9781837631667