Recommender Systems

Packt via Coursera Specialization

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Overview

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This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will teach you to build advanced recommender systems using machine learning and AI. You will begin by learning Python to evaluate datasets and create content-based and collaborative filtering systems. The specialization covers essential concepts like overfitting, bias, and variance, and introduces you to models such as KNN for building recommendation engines. As you progress, you'll dive deeper into deep learning models like RNNs, GRUs, and LSTMs, using TensorFlow to solve real-world problems. You’ll also learn how to apply advanced techniques like Restricted Boltzmann Machines (RBM) and Autoencoders in recommendation systems. The specialization includes practical projects, such as the Amazon Product Recommendation System, where you’ll analyze data, prepare it, and develop recommendation models using deep learning approaches. By the end of the specialization, you will be able to design and implement content-based and collaborative filtering recommender systems, apply deep learning models such as RNNs, and develop recommendation engines with TensorFlow. Ideal for aspiring data scientists and ML engineers. Basic Python knowledge and familiarity with neural networks and deep learning are helpful.

Syllabus

Course 1: Recommender Systems with Machine Learning
Course 2: Recommender Systems Complete Course Beginner to Advanced
Course 3: Recommender Systems: An Applied Approach using Deep Learning
Course 4: Building Recommender Systems with Machine Learning and AI

Courses

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14 hours 23 minutes
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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll explore the inner workings of recommender systems, gaining hands-on experience with Python and various machine learning techniques. Starting with the basics, you'll quickly move to more advanced methods like content-based filtering, collaborative filtering, and matrix factorization. By building real-world systems, you'll develop the skills needed to evaluate and improve recommender system performance. As you advance, you'll dive into deep learning for recommender systems, experimenting with technologies like Restricted Boltzmann Machines (RBM) and Autoencoders. You'll also explore TensorFlow Recommenders and other state-of-the-art approaches for building scalable recommendation engines. This course is designed to help you build, test, and deploy sophisticated recommender systems that can be applied in various industries. This course is ideal for those interested in artificial intelligence, machine learning, and data science, especially those who want to build personalized systems to enhance user experience. It will benefit anyone looking to design, evaluate, and optimize recommendation algorithms, making it an excellent resource for aspiring data scientists, machine learning engineers, and AI specialists.
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8 hours 15 minutes
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This course starts with the theoretical concepts and fundamental knowledge of recommender systems, covering essential taxonomies. You'll learn to use Python to evaluate datasets based on user ratings, choices, genres, and release years. Practical approaches will help you build content-based and collaborative filtering techniques. As you progress, you'll cover necessary concepts for applied recommender systems and machine learning models, with projects included for hands-on experience. Key learnings include AI-integrated basics, taxonomy, overfitting, underfitting, bias, variance, and building content-based and item-based systems with ML and Python, including KNN-based engines. The course is suitable for beginners and those with some programming experience, aiming to advance ML skills and build customized recommender systems. No prior knowledge of recommender systems, ML, data analysis, or math is needed, only basic Python. By the end, you'll relate theories to various domains, implement ML models for real-world recommendation systems, and evaluate them.
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9 hours 58 minutes
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the 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.
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3 hours 45 minutes
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for product recommendations, prepare data, and model development using a two-tower approach. You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system. Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems. This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.