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
category
Recommender Systems and Deep Learning in Python
33 004
students
12.5 hours
content
May 2025
last update
$109.99
regular price

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

Recommender Systems and Deep Learning in Python – Screenshot 1
Screenshot 1Recommender Systems and Deep Learning in Python
Recommender Systems and Deep Learning in Python – Screenshot 2
Screenshot 2Recommender Systems and Deep Learning in Python
Recommender Systems and Deep Learning in Python – Screenshot 3
Screenshot 3Recommender Systems and Deep Learning in Python
Recommender Systems and Deep Learning in Python – Screenshot 4
Screenshot 4Recommender 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
1899124
udemy ID
06/09/2018
course created date
31/07/2019
course indexed date
Bot
course submited by
Recommender Systems and Deep Learning in Python - | Comidoc