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Machine Learning for Recommender Systems: Making Recommendations

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Packt

Recommender Systems Complete Course Beginner to Advanced

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Updated in May 2025. This course now features 糖心vlog官网观看 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.

Skills You'll Learn

Deep Learning, Artificial Neural Networks, Tensorflow, Natural Language Processing, Time Series Analysis and Forecasting, Predictive Modeling, Keras (Neural Network Library), Applied Machine Learning, Machine Learning Algorithms, Machine Learning

From the lesson

Recommender Systems with Machine Learning

Motivation for Recommender System: Recommender Systems Overview2:51

Motivation for Recommender System: Introduction to Recommender Systems3:13

Motivation for Recommender System: Recommender Systems Process and Goals5:09

Motivation for Recommender System: Generations of Recommender Systems2:54

Motivation for Recommender System: Nexus of AI and Recommender Systems6:02

Motivation for Recommender System: Applications and Real-World Challenges4:38

Motivation for Recommender System: Quiz0:39

Motivation for Recommender System: Quiz Solution2:07

Basics of Recommender System: Overview4:03

Basics of Recommender System: Taxonomy of Recommender Systems9:11

Basics of Recommender System: ICM4:17

Basics of Recommender System: User Rating Matrix5:36

Basics of Recommender System: Quality of Recommender System11:19

Basics of Recommender System: Online Evaluation Techniques6:22

Basics of Recommender System: Offline Evaluation Techniques5:27

Basics of Recommender System: Data Partitioning6:56

Basics of Recommender System: Important Parameters3:35

Basics of Recommender System: Error Metric Computation5:24

Basics of Recommender System: Content-Based Filtering4:39

Basics of Recommender System: Collaborative Filtering and User-Based Collaborative Filtering5:28

Basics of Recommender System: Item Model and Memory-Based Collaborative Filtering6:12

Basics of Recommender System: Quiz0:41

Basics of Recommender System: Quiz Solution1:48

Machine Learning for Recommender Systems: Overview3:06

Machine Learning for Recommender Systems: Benefits of Machine Learning7:52

Machine Learning for Recommender Systems: Guidelines for ML5:12

Machine Learning for Recommender Systems: Design Approaches for ML5:10

Machine Learning for Recommender Systems: Content-Based Filtering3:04

Machine Learning for Recommender Systems: Data Preparation for Content-Based Filtering7:58

Machine Learning for Recommender Systems: Data Manipulation for Content-Based Filtering11:52

Machine Learning for Recommender Systems: Exploring Genres in Content-Based Filtering12:31

Machine Learning for Recommender Systems: tf-idf Matrix11:08

Machine Learning for Recommender Systems: Recommendation Engine9:25

Machine Learning for Recommender Systems: Making Recommendations8:49

Machine Learning for Recommender Systems: Item-Based Collaborative Filtering4:56

Machine Learning for Recommender Systems: Item-Based Filtering Data Preparation13:04

Machine Learning for Recommender Systems: Age Distribution for Users7:32

Machine Learning for Recommender Systems: Collaborative Filtering using KNN16:58

Machine Learning for Recommender Systems: Geographic Filtering3:35

Machine Learning for Recommender Systems: KNN Implementation9:08

Machine Learning for Recommender Systems: Making Recommendations with Collaborative Filtering12:26

Machine Learning for Recommender Systems: User-Based Collaborative Filtering2:44

Machine Learning for Recommender Systems: Quiz0:35

Machine Learning for Recommender Systems: Quiz Solution2:37

Project 1: Song Recommendation System Using Content-Based Filtering: Project Introduction2:19

Project 1: Song Recommendation System Using Content-Based Filtering: Dataset Usage4:57

Project 1: Song Recommendation System Using Content-Based Filtering: Missing Values5:12

Project 1: Song Recommendation System Using Content-Based Filtering: Exploring Genres7:10

Project 1: Song Recommendation System Using Content-Based Filtering: Occurrence Count6:24

Project 1: Song Recommendation System Using Content-Based Filtering: tf-idf Implementation5:53

Project 1: Song Recommendation System Using Content-Based Filtering: Similarity Index2:06

Project 1: Song Recommendation System Using Content-Based Filtering: Fuzzywuzzy Implementation4:08

Project 1: Song Recommendation System Using Content-Based Filtering: Find Closest Title4:13

Project 1: Song Recommendation System Using Content-Based Filtering: Making Recommendations9:56

Project 2: Movie Recommendation System Using Collaborative Filtering: Project Introduction2:05

Project 2: Movie Recommendation System Using Collaborative Filtering: Dataset Discussion5:36

Project 2: Movie Recommendation System Using Collaborative Filtering: Rating Plot5:08

Project 2: Movie Recommendation System Using Collaborative Filtering: Count5:12

Project 2: Movie Recommendation System Using Collaborative Filtering: Logarithm of Count5:25

Project 2: Movie Recommendation System Using Collaborative Filtering: Active Users and Popular Movies8:53

Project 2: Movie Recommendation System Using Collaborative Filtering: Create Collaborative Filter4:49

Project 2: Movie Recommendation System Using Collaborative Filtering: KNN Implementation5:17

Project 2: Movie Recommendation System Using Collaborative Filtering: Making Recommendations6:02

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