Recommender Systems Complete Course Beginner to Advance

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

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

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

If Yes! Then this is the course you are looking for.

You might have searched for many relevant courses, but this course is different!

This course is a complete package for the beginners to learn the basics of recommender systems, its applications and building it from scratch by using machine learning and deep learning with python. Every module has engaging content covering necessary theoretical concepts with a complete practical approach is used in along with brief theoretical concepts.  At the end of every module, we assign you a quiz, the solution to the quizzes is also available in the next video.

We will be starting with the theoretical concepts of recommender systems, after providing you the basic knowledge of recommender systems. You will be able to learn about the important taxonomies of recommender systems which are the basic building block of it.  

This complete package will enable you to learn the basic to advance mechanism of developing recommender system by using machine learning and deep learning with python. We’ll be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine leaning. Python will be taught from elementary level up to an advanced level so that any machine learning and deep learning concepts can be implemented.

This comprehensive course will be your guide to 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. Moreover, a practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems where hands on experience will be developed.

We’ll learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning and deep learning models. Moreover, various projects have been included in this course to develop a very useful experience for yourselves.

Machine learning has been ranked as one of the hottest jobs on Glassdoor, and the average salary of a machine learning engineer is over $110,000 in the United States, according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or even those who know nothing about Data Analysis, ML and RNNs!

This comprehensive course is comparable to other Recommender Systems using Machine Learning and Deep Learning courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost in only one course! With over 6 hours of HD video lectures that are divided into many videos and detailed code notebooks for every address this is one of the most comprehensive courses for Recommender Systems using Machine Learning and Deep Learning on Tutorialspoint!

Why Should You Enroll in This Course?

The course is crafted to help you understand not only the role and impact of recommender systems in real world applications, but it provides a very unique hands on experience on developing complete recommender systems engines for your customized dataset by using various projects. This straightforward learning by doing course will help you in mastering the concepts and methodology with regards to Python. 

This course is:

Easy to understand.

Expressive and self-explanatory

To the point

Practical with live coding

A complete package with three in depth projects covering complete course contents

Thorough, covering the most advanced and recently discovered machine learning models by renowned data scientists and AI practitioners

Teaching Is Our Passion:

We focus on creating online tutorials that encourage learning by doing. We aim to provide you with more than a superficial look at practical approach towards building recommender systems using machine learning from the perspective of content-based filtering and collaborative filtering. For instance, this course has two projects in the final module which will help you to see for yourself via experimentation the practical implementation of machine learning with data analysis on the real-world datasets of movies and Spotify songs. We have worked extra hard to ensure you understand the concepts clearly. We want you to have a sound understanding of the basics before you move onward to the more complex concepts. The course materials that make certain you accomplish all this include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. You can also get in touch with our friendly team in case of any queries.

Course Content:

We'll teach you how to program with Python, how to use machine learning concepts to develop recommender systems! Here are just a few of the topics that we will be learning:

Course Overview

Motivation for Recommender Systems

Recommender Systems Process

Goals of Recommender Systems 

Generations of Recommender Systems

Nexus of Recommender Systems with Artificial Intelligence

Real World Challenges of Recommender Systems

Applications of Recommender Systems

Basics of Recommender Systems

Taxonomy of Recommender Systems

Item-context Matrix

User-Rating Matrix

Inferring Preferences

Quality of Recommender Systems

Online and Offline Evaluation Techniques

Dataset Partitioning

Overfitting

Error Matrix

Content-based Filtering

Collaborative Filtering

User-based and Item-based Collaborative Filtering

Recommender Systems with Machine Learning

Machine Learning in Recommender Systems

Benefits of Machine Learning in Recommender Systems

Design Approaches for Recommender Systems using Machine Learning

Guidelines for Machine Learning based Recommender Systems

Hands on- Practical Approach for Content Based Filtering using Machine Learning

Hands on- Practical Approach for Item based Collaborative Filtering using Machine Learning

Project 1: Songs Recommendation System for a Music Application using Machine Learning

Project 2: Movie Recommendation System using K-nearest Neighbors Algorithm

Deep Learning for Recommender Systems

Overview of Deep Learning in Recommendation Systems

Benefits and Challenges of Deep Learning in Recommender Systems

Deep Learning for Recommendation Inference

A Generic Deep Learning based Recommendation Approach

Neutral Collaborative Filtering 

Project: Amazon Product Recommendation System

Packages Installation

Data Analysis for Products Recommendation

Data Preparation

Model Development using Two-tower Approach

Implementing TensorFlow Recommenders

Fitting and Evaluation or Recommender System

Validation of Recommender System

Testing a Recommender Model

Making Predictions using Recommender Systems

Enroll in the course and become a recommender systems expert today!

After completing this course successfully, you will be able to:

Relate the concepts and theories for recommender systems in various domains

Understand and implement machine learning models for building real world recommendation systems

Understand and implement deep learning models for building real world recommendation systems

Understand evaluate the machine learning and deep learning models

Who this course is for:

People who want to advance their skills in applied machine learning and deep learning

People who want to master relation of data analysis with machine learning and deep learning

People who want to build customized recommender systems for their applications

People who want to implement machine learning and deep learning algorithms for recommender systems

Individuals who are passionate about recommender systems specially content based and collaborative filtering-based recommenders and two tower based recommender systems

Machine Learning and Deep Learning Practitioners

Research Scholars

Data Scientists