Recommender Systems and Deep Learning in Python
Recommender Systems and Deep Learning in Python Course
The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
The Recommender Systems and Deep Learning in Python course on Udemy is a comprehensive course that covers a wide range of topics in deep learning, machine learning, data science, and AI techniques. The course is designed for both beginners and experts, and it covers everything from the basics of recommendation systems to advanced topics like matrix factorization, deep learning, and more.
The course is taught by the Lazy Programmer Team, who are experts in the field of data science. The course covers a wide range of topics, including how to 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, and more.
The course is designed to be hands-on, so you’ll be able to apply what you learn to real-world problems. You’ll learn how to build a recommendation system using popular news feed algorithms like Reddit, Hacker News, and Google PageRank. You’ll also learn how to use Bayesian recommendation techniques that are being used by a large number of media companies today. Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now. These algorithms have led to billions of dollars in added revenue.
Overall, the Recommender Systems and Deep Learning in Python course on Udemy is an excellent resource for anyone who wants to learn about recommendation systems with deep learning. It’s comprehensive, well-structured, and taught by experts in the field.
What you’ll learn in Recommender Systems and Deep Learning in Python Course
- 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
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Who this course is for:
- Anyone who owns or operates an Internet business
- Students in machine learning, deep learning, artificial intelligence, and data science
- Professionals in machine learning, deep learning, artificial intelligence, and data science
