Recommender Systems Complete Course Beginner to Advanced

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Recommender Systems Complete Course Beginner to Advanced

Instructor: Packt - Course Instructors

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

3 weeks at 3 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

3 weeks at 3 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Identify the fundamental concepts of sequence data and time series forecasting.
Explain the workings of autoregressive linear models and simple RNNs.
Implement GRU and LSTM units for various prediction tasks using TensorFlow.
Differentiate between simple RNNs, GRU, and LSTM units.

Details to know

Shareable certificate

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Assessments

1 assignment

Taught in English

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There are 3 modules in this course

Dive into the world of Recurrent Neural Networks (RNNs) with this in-depth course designed to equip you with essential knowledge and hands-on skills using TensorFlow. Start with an introduction to the core concepts of sequence data and time series forecasting, then progress to understanding and implementing autoregressive linear models. Discover how to apply simple RNNs to solve many-to-one and many-to-many problems, with practical coding sessions in TensorFlow 2.

Move beyond basics with modern RNN units like GRU and LSTM, mastering their application in complex signal prediction and overcoming long-distance dependency issues. Learn the intricacies of RNN architecture and prepare to tackle more challenging tasks such as image classification and stock return predictions. The course emphasizes practical coding exercises, ensuring you can confidently implement these techniques in real-world scenarios. Finally, explore natural language processing (NLP) applications, including embeddings, text preprocessing, and text classification using LSTMs. This course is structured to provide a thorough understanding of RNNs, empowering you to apply these deep learning models effectively in various domains. This course is perfect for developers, data scientists, and tech enthusiasts who want to learn how to build and implement recommender systems. Basic knowledge of Python and machine learning concepts is recommended but not required.

In this module, we will introduce the instructor and provide an overview of the course. You'll learn about the course structure, the key concepts covered, and the differences between machine learning and deep learning recommender systems.

What's included

5 videos1 reading

In this module, we will explore the fundamentals of recommender systems, including their motivations, processes, and goals. You'll learn about different generations of recommender systems, their real-world applications, and the challenges they face. Additionally, this section covers various filtering techniques and their evaluation methods.

What's included

63 videos

63 videosTotal 368 minutes

Motivation for Recommender System: Recommender Systems Overview2 minutesPreview module
Motivation for Recommender System: Introduction to Recommender Systems3 minutes
Motivation for Recommender System: Recommender Systems Process and Goals5 minutes
Motivation for Recommender System: Generations of Recommender Systems2 minutes
Motivation for Recommender System: Nexus of AI and Recommender Systems6 minutes
Motivation for Recommender System: Applications and Real-World Challenges4 minutes
Motivation for Recommender System: Quiz0 minutes
Motivation for Recommender System: Quiz Solution2 minutes
Basics of Recommender System: Overview4 minutes
Basics of Recommender System: Taxonomy of Recommender Systems9 minutes
Basics of Recommender System: ICM4 minutes
Basics of Recommender System: User Rating Matrix5 minutes
Basics of Recommender System: Quality of Recommender System11 minutes
Basics of Recommender System: Online Evaluation Techniques6 minutes
Basics of Recommender System: Offline Evaluation Techniques5 minutes
Basics of Recommender System: Data Partitioning6 minutes
Basics of Recommender System: Important Parameters3 minutes
Basics of Recommender System: Error Metric Computation5 minutes
Basics of Recommender System: Content-Based Filtering4 minutes
Basics of Recommender System: Collaborative Filtering and User-Based Collaborative Filtering5 minutes
Basics of Recommender System: Item Model and Memory-Based Collaborative Filtering6 minutes
Basics of Recommender System: Quiz0 minutes
Basics of Recommender System: Quiz Solution1 minute
Machine Learning for Recommender Systems: Overview3 minutes
Machine Learning for Recommender Systems: Benefits of Machine Learning7 minutes
Machine Learning for Recommender Systems: Guidelines for ML5 minutes
Machine Learning for Recommender Systems: Design Approaches for ML5 minutes
Machine Learning for Recommender Systems: Content-Based Filtering3 minutes
Machine Learning for Recommender Systems: Data Preparation for Content-Based Filtering7 minutes
Machine Learning for Recommender Systems: Data Manipulation for Content-Based Filtering11 minutes
Machine Learning for Recommender Systems: Exploring Genres in Content-Based Filtering12 minutes
Machine Learning for Recommender Systems: tf-idf Matrix11 minutes
Machine Learning for Recommender Systems: Recommendation Engine9 minutes
Machine Learning for Recommender Systems: Making Recommendations8 minutes
Machine Learning for Recommender Systems: Item-Based Collaborative Filtering4 minutes
Machine Learning for Recommender Systems: Item-Based Filtering Data Preparation13 minutes
Machine Learning for Recommender Systems: Age Distribution for Users7 minutes
Machine Learning for Recommender Systems: Collaborative Filtering using KNN16 minutes
Machine Learning for Recommender Systems: Geographic Filtering3 minutes
Machine Learning for Recommender Systems: KNN Implementation9 minutes
Machine Learning for Recommender Systems: Making Recommendations with Collaborative Filtering12 minutes
Machine Learning for Recommender Systems: User-Based Collaborative Filtering2 minutes
Machine Learning for Recommender Systems: Quiz0 minutes
Machine Learning for Recommender Systems: Quiz Solution2 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Project Introduction2 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Dataset Usage4 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Missing Values5 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Exploring Genres7 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Occurrence Count6 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: tf-idf Implementation5 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Similarity Index2 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Fuzzywuzzy Implementation4 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Find Closest Title4 minutes
Project 1: Song Recommendation System Using Content-Based Filtering: Making Recommendations9 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Project Introduction2 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Dataset Discussion5 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Rating Plot5 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Count5 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Logarithm of Count5 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Active Users and Popular Movies8 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Create Collaborative Filter4 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: KNN Implementation5 minutes
Project 2: Movie Recommendation System Using Collaborative Filtering: Making Recommendations6 minutes

In this module, we will delve into the application of deep learning techniques in recommender systems. You'll learn about foundational concepts, inference mechanisms, and different deep learning models, such as neural collaborative filtering and variational autoencoders. This module also includes a project on building an Amazon product recommendation system using TensorFlow.

What's included

26 videos1 assignment

26 videosTotal 116 minutes

Deep Learning Foundation for Recommender Systems: Module Introduction2 minutesPreview module
Deep Learning Foundation for Recommender Systems: Overview3 minutes
Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation systems3 minutes
Deep Learning Foundation for Recommender Systems: Inference After Training3 minutes
Deep Learning Foundation for Recommender Systems: Inference Mechanism3 minutes
Deep Learning Foundation for Recommender Systems: Embeddings and User Context5 minutes
Deep Learning Foundation for Recommender Systems: Neural Collaborative Filtering3 minutes
Deep Learning Foundation for Recommender Systems: VAE Collaborative Filtering3 minutes
Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL Models3 minutes
Deep Learning Foundation for Recommender Systems: Deep Learning Quiz0 minutes
Deep Learning Foundation for Recommender Systems: Deep Learning Quiz Solution1 minute
Project Amazon Product Recommendation System: Module Overview1 minute
Project Amazon Product Recommendation System: TensorFlow Recommenders1 minute
Project Amazon Product Recommendation System: Two-Tower Model2 minutes
Project Amazon Product Recommendation System: Project Overview1 minute
Project Amazon Product Recommendation System: Download Libraries4 minutes
Project Amazon Product Recommendation System: Data Visualization with WordCloud8 minutes
Project Amazon Product Recommendation System: Make Tensors from DataFrame6 minutes
Project Amazon Product Recommendation System: Rating Our Data6 minutes
Project Amazon Product Recommendation System: Random Train-Test Split5 minutes
Project Amazon Product Recommendation System: Making the Model and Query Tower8 minutes
Project Amazon Product Recommendation System: Candidate Tower and Retrieval System5 minutes
Project Amazon Product Recommendation System: Compute Loss3 minutes
Project Amazon Product Recommendation System: Train and Validation11 minutes
Project Amazon Product Recommendation System: Accuracy Versus Recommendations8 minutes
Project Amazon Product Recommendation System: Making Recommendations7 minutes

1 assignmentTotal 60 minutes

Full Course Assessment60 minutes

Instructor

Packt - Course Instructors

Packt

568 Courses78,543 learners

Offered by

Packt

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

Recommender Systems Complete Course Beginner to Advanced

What you'll learn

Details to know

See how employees at top companies are mastering in-demand skills

Earn a career certificate

There are 3 modules in this course

Introduction

What's included

5 videosTotal 8 minutes

1 readingTotal 10 minutes

Recommender Systems with Machine Learning

What's included

63 videosTotal 368 minutes

Deep Learning for Recommender Systems: An Applied Approach

What's included

26 videosTotal 116 minutes

1 assignmentTotal 60 minutes

Instructor

Offered by

Recommended if you're interested in Machine Learning

Basic Recommender Systems

Deep Learning - Recurrent Neural Networks with TensorFlow

Advanced Recommender Systems