Recommender Systems and Deep Learning in Python

The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques

4.68 (5960 reviews)

Udemy

platform

English

language

Data & Analytics

What you will learn

Understand and implement accurate recommendations for your users using simple and state-of-the-art algorithms

Big data matrix factorization on Spark with an AWS EC2 cluster

Matrix factorization / SVD in pure Numpy

Matrix factorization in Keras

Deep neural networks, residual networks, and autoencoder in Keras

Restricted Boltzmann Machine in Tensorflow

Course Gallery

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Recommender Systems and Deep Learning in Python – Screenshot 4 — Screenshot 4 – Recommender Systems and Deep Learning in Python

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Comidoc Review

Our Verdict

Recommender Systems and Deep Learning in Python offers a thorough exploration of various recommender system approaches for those with strong mathematical and programming foundations. The course excels in diving deep into complex algorithms while maintaining a clear and engaging delivery. However, there are a few areas that could be improved, such as the availability of code notebooks, pacing adjustments, and enhanced practical guidance for real-world applications.

What We Liked

Covers a wide range of recommender system approaches with clear explanations and implementations
In-depth exploration of key algorithms, including matrix factorization, SVD, deep neural networks, and RBMs
Real-world applicable content, illustrated with big data matrix factorization on Spark with an AWS EC2 cluster
Provides theoretical explanations for complex topics, making it suitable for those with a mathematical background

Potential Drawbacks

Some issues with code notebook availability and accessibility for certain users
Minor concerns about the pacing and tone in some parts of the course
Lack of real-world job application guidance and comprehensive evaluation metrics coverage
Occasional gaps in connecting theory to practice, especially in the PageRank section