Description

The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.

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. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Recommender-Systems-with-Machine-Learning

What You 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.

Approach

This is a comprehensive, easy-to-understand, self-explanatory, to-the-point, and practical course with live coding and two in-depth projects covering complete course contents.

Every module has engaging content; a completely practical approach is used along with brief theoretical concepts. At the end of every module, there will be a quiz, followed by its solution in the next video.

Key Features

Build recommender systems using ML from the perspective of content-based and collaborative filtering * Implementation of ML with data analysis on real-world datasets of movies and Spotify songs * Learn to program with Python and how to use ML concepts to develop recommender systems

Github Repo

https://github.com/PacktPublishing/Recommender-Systems-with-Machine-Learning

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.

Course Outline

1. Introduction

1. AI Sciences Introduction

This video provides an introduction to AI Sciences.

2. Instructor Introduction

This video provides an introduction to the instructor in detail. You will look at the course's structure and the things you will learn by the end of this course.

3. Overview of Recommender Systems

This video provides an overview of the recommender systems and their importance.

4. Fundamentals of Recommender Systems

This video helps you out with the fundamentals of recommender systems.

5. Project Overview

This video discusses the projects you will be working on in the later sections of this course.

2. Motivation for Recommender System

1. Recommender Systems Overview

This video provides an overview of recommender systems.

2. Introduction to Recommender Systems

In this video, you will get to know more about recommender systems.

3. Recommender Systems Process and Goals

This video helps you understand the process and goals of recommender systems.

4. Generations of Recommender Systems

This video helps you with generations of recommender systems.

5. Nexus of AI and Recommender Systems

This video talks about the nexus of AI and recommender systems.

6. Applications and Real-World Challenges

This video helps you understand the applications and real-world challenges, and how recommender systems resolve these issues.

7. Quiz

This is a quiz video on the concepts learned in this module/section.

8. Quiz Solution

This is a solution video of the quiz for this module/section.

3. Basic of Recommender Systems

1. Section Overview

This video provides an overview of the section.

2. Taxonomy of Recommender Systems

In this video, you will study the taxonomy of recommender systems.

3. ICM

This video talks about the ICM (Item Content Matrix) that is used to store the data of recommender systems.

4. User Rating Matrix

This video talks about URM (User Rating Matrix), which is used to store the data of recommender systems.

5. Quality of Recommender Systems

This video explains the quality of recommender systems in detail.

6. Online Evaluation Techniques

This video talks about the online evaluation techniques of recommender systems.

7. Offline Evaluation Techniques

This video talks about offline evaluation techniques of recommender systems.

8. Data Partitioning

This video explains data partitioning.

9. Important Parameters

This video talks about the important parameters of recommender systems.

10. Error Metric Computation

This video demonstrates error metric computation.

11. Content-Based Filtering

This video helps you with content-based filtering.

12. Collaborative Filtering and User-Based Collaborative Filtering

This video helps you with collaborative filtering and user-based collaborative filtering.

13. Item Model and Memory-Based Collaborative Filtering

This video helps you with the item model and memory-based collaborative filtering.

14. Quiz

This is a quiz video on the concepts learned in this module/section.

15. Quiz Solution

This is a solution video of the quiz for this module/section.

4. Machine Learning for Recommender System

1. Overview

This video provides an overview of the section.

2. Benefits of Machine Learning

This video helps you with the benefits of machine learning.

3. Guidelines for ML

This video demonstrates the guidelines for machine learning.

4. Design Approaches for ML

This video talks about the design approaches for machine learning.

5. Content-Based Filtering

This video helps you with content-based filtering.

6. Data Preparation for Content-Based Filtering

This video explains data preparation for content-based filtering with machine learning.

7. Data Manipulation for Content-Based Filtering

This video explains data manipulation for content-based filtering with machine learning.

8. Exploring Genres in Content-Based Filtering

This video helps you explore the genres in content-based filtering.

9. tf-idf (Term Frequency-Inverse Document Frequency) Matrix

This video is about the tf-idf (Term Frequency-Inverse Document Frequency) matrix.

10. Recommendation Engine

This video talks about the recommendation engine in detail.

11. Making Recommendations

This video helps you with making recommendations.

12. Item-Based Collaborative Filtering

This video demonstrates item-based collaborative filtering.

13. Item-Based Filtering Data Preparation

This video explains and demonstrates item-based filtering data preparation.

14. Age Distribution for Users

This video helps you find the age distribution for users from the dataset.

15. Collaborative Filtering Using KNN

This video helps you with collaborative filtering using KNN.

16. Geographic Filtering

This video helps you with geographic filtering.

17. KNN Implementation

This video explains how to implement KNN.

18. Making Recommendations with Collaborative Filtering

This video helps you make recommendations with collaborative filtering.

19. User-Based Collaborative Filtering

This video helps you with user-based collaborative filtering.

20. Quiz

This is a quiz video on the concepts learned in this module/section.

21. Quiz Solution

This is a solution video of the quiz for this module/section.

5. Project 1: Song Recommendation System Using Content-Based Filtering

1. Project Introduction

This video gives you an overview of the project.

2. Dataset Usage

This video explains how to use the dataset to your advantage.

3. Missing Values

This video explains how to churn out the missing values from the dataset.

4. Exploring Genres

This video helps you with exploring genres from the dataset.

5. Occurrence Count

This video demonstrates the occurrence count of the specific condition.

6. tf-idf (Term Frequency-Inverse Document Frequency) Implementation

This video demonstrates the implementation of tf-idf (Term Frequency-Inverse Document Frequency).

7. Similarity Index

This video talks about the similarity index in detail.

8. FuzzyWuzzy Implementation

This video helps you with the implementation of the FuzzyWuzzy algorithm.

9. Find the Closest Title

This video explains how to find the closest title from the dataset as per the input.

10. Making Recommendations

This video explains how to make song recommendations.

6. Project 2: Movie Recommendation System Using Collaborative Filtering

1. Project Introduction

This video provides an introduction to the project.

2. Dataset Discussion

This video involves a deep understanding of the dataset to be explored for this module.

3. Rating Plot

This video helps you with the plot rating from the dataset.

4. Count

This video explains the count function.

5. Logarithm of Count

This video helps you explain the logarithm of count.

6. Active Users and Popular Movies

This video explains the active users and popular movies available.

7. Create Collaborative Filter

This video demonstrates creating a collaborative filter.

8. KNN Implementation

This video explains the implementation of KNN.

9. Making Recommendations

This video explains how to make movie recommendations.

10. Course Conclusion

This video provides the conclusion for the course. We hope that you will be able to apply the learnings from this course into your professional life.

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