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Instructor: Packt - Course Instructors
Included with
Recommended experience
Intermediate level
This course is for developers and engineers looking to build recommender systems using ML, DL, and AI. Python required.
Recommended experience
Intermediate level
This course is for developers and engineers looking to build recommender systems using ML, DL, and AI. Python required.
Analyze and evaluate recommendation algorithms using Python.
Create session-based recommendations using recurrent neural networks.
Implement large-scale recommendation computations with Apache Spark.
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September 2024
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This course teaches you to use Python, AI, machine learning, and deep learning to build recommender systems, from simple engines to hybrid ensemble recommenders. You'll start with an introduction to recommender systems and Python, evaluate systems, and explore the recommender engine framework.
You'll learn content-based recommendations, neighborhood-based collaborative filtering, and methods like matrix factorization and SVD. The course covers applying deep learning and AI to recommendations, scaling datasets with Apache Spark, solving real-world challenges, and studying systems like YouTube and Netflix. By the end, you'll build recommendation systems to help users discover new products and content. You'll test and evaluate algorithms with Python, use K-Nearest-Neighbors, address large-scale issues, make session-based recommendations with neural networks, and compute recommendations with Apache Spark. This course is for developers with basic Python knowledge.
In this module, we will lay the foundation for the course by setting up the development environment with Anaconda, familiarizing you with the course materials, and introducing you to creating simple movie recommendations.
7 videos1 reading
In this module, we will cover the essentials of Python programming, including basic syntax, data structures, and functions. We will also delve into Boolean expressions and loops through hands-on challenges.
4 videos
In this module, we will explore various methods for evaluating recommender systems, including accuracy metrics, hit rates, and diversity measures. We will also review practical examples and quizzes to reinforce learning.
9 videos1 assignment
In this module, we will focus on the architecture of a recommender engine framework, guiding you through code walkthroughs and activities to implement and test various recommendation algorithms.
4 videos
In this module, we will dive into content-based filtering methods, exploring metrics like cosine similarity and KNN. We will also conduct hands-on activities to produce and evaluate movie recommendations.
6 videos
In this module, we will cover neighborhood-based collaborative filtering techniques, including user-based and item-based methods. Practical exercises and activities will help solidify your understanding of these approaches.
13 videos1 assignment
In this module, we will explore matrix factorization methods like PCA and SVD, demonstrating how to apply these techniques to movie rating datasets. We will also focus on improving these methods through hyperparameter tuning.
6 videos
In this module, we will provide an optional deep dive into deep learning, covering fundamental concepts, neural network architectures, and practical implementations using TensorFlow and Keras.
25 videos
In this module, we will focus on applying deep learning to recommender systems, exploring techniques like Restricted Boltzmann Machines (RBM) and auto-encoders. We will also cover practical evaluation and tuning methods.
19 videos1 assignment
In this module, we will explore methods to scale up recommendation systems, including using Apache Spark for large-scale data processing and Amazon's DSSTNE and SageMaker for deploying scalable machine learning models.
11 videos
In this module, we will tackle real-world challenges faced by recommender systems, such as the cold start problem, filtering bubbles, and fraud. We will also explore solutions to these issues through practical exercises.
11 videos1 assignment
In this module, we will study real-world case studies of YouTube and Netflix, focusing on their recommendation strategies and the use of deep learning and hybrid approaches to enhance recommendation quality.
4 videos
In this module, we will explore hybrid recommendation approaches, combining multiple algorithms to improve recommendation accuracy and diversity. Practical exercises will guide you through implementing and evaluating hybrid systems.
2 videos1 assignment
In this module, we will wrap up the course by summarizing key points, providing resources for further study, and introducing advanced topics and emerging trends in recommender systems to keep you up-to-date.
1 video1 assignment
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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University of Minnesota
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Sungkyunkwan University
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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.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Financial aid available,