Building Recommender Systems with Machine Learning and AI

How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.

4.66 (3476 reviews)

Udemy

platform

English

language

Software Engineering

What you will learn

Understand and apply user-based and item-based collaborative filtering to recommend items to users

Create recommendations using deep learning at massive scale

Build recommendation engines with neural networks and Restricted Boltzmann Machines (RBM's)

Make session-based recommendations with recurrent neural networks and Gated Recurrent Units (GRU)

Build a framework for testing and evaluating recommendation algorithms with Python

Apply the right measurements of a recommender system's success

Build recommender systems with matrix factorization methods such as SVD and SVD++

Apply real-world learnings from Netflix and YouTube to your own recommendation projects

Combine many recommendation algorithms together in hybrid and ensemble approaches

Use Apache Spark to compute recommendations at large scale on a cluster

Use K-Nearest-Neighbors to recommend items to users

Solve the "cold start" problem with content-based recommendations

Understand solutions to common issues with large-scale recommender systems

Course Gallery

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Comidoc Review

Our Verdict

Building Recommender Systems with Machine Learning and AI is an insightful course that offers a wide-ranging exploration of various recommendation algorithms while enriching the learning experience with real-world examples. However, its potential is hindered by the absence of visual aids and adequate code explanation depth, leaving room for improvement in helping learners grasp complex concepts more intuitively.

What We Liked

Comprehensive coverage of various recommendation algorithms and techniques, including collaborative filtering, deep learning, matrix factorization methods, hybrid models, and K-Nearest-Neighbors
Exposes students to real-world learnings from platforms like Netflix and YouTube, enabling better understanding and practical application
Incorporates a wide range of advanced topics, making it an excellent resource for those looking to delve deeper into the field
Provides useful code samples, enabling students to implement and experiment with recommendation systems concepts

Potential Drawbacks

Lacks visual aids and animated cues to facilitate better comprehension, causing some of the explanations to feel complex and lengthy
Important applications such as the 'hybrid model' are briefly covered, leaving students wanting more in-depth treatment
The course might benefit from the inclusion of graded assignments or quizzes to help students better understand the presented algorithms
Code explanations could be improved with more patience and detailed walkthroughs, making it easier for beginners to follow along