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Building Recommender Systems with Machine Learning and AI Course Drive

Building Recommender Systems with Machine Learning and AI Course Drive Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

Building Recommender Systems with Machine Learning and AI Course Drive

Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

What you’ll learn

Building Recommender Systems with Machine Learning and AI Course Drive

  • Understand and apply user-based and item-based collaborative filtering to recommend items to users
  • Create recommendations using deep learning at massive scale
  • Build recommender systems with neural networks and Restricted Boltzmann Machines (RBMs)
  • 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
  • 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

Requirements

  • Some experience with a programming or scripting language (preferably Python)
  • Some computer science background, and an ability to understand new algorithms.

Description

Learn how to build recommender systems from one of Amazon’s pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation technologies. Course Drive

You’ve seen automated recommendations everywhere – on Netflix’s home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you’ll become very valuable to them.

We’ll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks.

Recommender systems are complex; don’t enroll in this course expecting a learn-to-code type of format. There’s no recipe to follow on how to make a recommender system; We assume you already know how to code. AI Course Drive

You’ll develop your own framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We’ll cover:

  • Building a recommendation engine
  • Evaluating recommender systems
  • Content-based filtering using item attributes
  • Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
  • Model-based methods including matrix factorization and SVD
  • Applying deep learning, AI, and artificial neural networks to recommendations
  • Session-based recommendations with recursive neural networks
  • Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines
  • Real-world challenges and solutions with recommender systems
  • Case studies from YouTube and Netflix
  • Building hybrid, ensemble recommenders

This comprehensive course takes you all the way from the early days of collaborative filtering to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.

The coding exercises in this course use the Python programming language.

High-quality, hand-edited English closed captions are included to help you follow along.

Who this course is for:

  • Software developers interested in applying machine learning and deep learning to the product or content recommendations
  • Engineers working at or interested in working at large e-commerce or web companies
  • Computer Scientists interested in the latest recommender system theory and research
  • Content From: https://www.udemy.com/course/building-recommender-systems-with-machine-learning-and-ai/
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