Recommender System With Machine Learning and Statistics

Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fastai and Python

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Recommender System With Machine Learning and Statistics
Recommender System With Machine Learning and Statistics

Recommender System With Machine Learning and Statistics udemy course

Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fastai and Python

What you'll 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 recommender systems 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

Requirements:

  • A Windows, Mac, or Linux PC with at least 3GB of free disk space.
  • Some experience with a programming or scripting language (preferably Python)
  • Some computer science background, and an ability to understand new algorithms.

Description:

Recommender system is a promising approach to boost sales to the next level by suggesting the right products to the right customers.

This course starts by showing you the main solutions of recommender systems in the industry and the hypotheses behind the main solutions. You’ll then learn how to build collaborative filtering models with fastai, and exercise the trained model on test datasets. Recommender System With Machine Learning and Statistics Udemy

As you advance, you’ll visualize latent features, interpret weights and biases, and check what similar users/Items are from the model’s perspective. Furthermore, you’ll build a hybrid recommender system with popularity and association rule, and evaluate the recommendations with selected criteria.

By the end of this course, you’ll be able to explain the theories and assumptions of recommender systems and build your own recommender on other datasets using python. The outline of course is as follows:

  • Why Business Needs Recommender Systems

  • Roadmap of the Course

  • The Hypotheses Behind the Main Solutions of Recommender Systems

  • Hands-on Collaborative Filtering Recommender System With Fastai on Instacart Grocery Dataset

    • A Quick Eda on the Grocery Dataset

    • What Is Collaborative Filtering in Depth

    • How to Build and Train Collaborative Filtering Model With Fastai

    • How to Visualize Latent Features? Do Popular Items Have a Higher Bias? What Are Similar Users From Model Perspective?

  • Step-By-Step Guide to Build a Hybrid Recommender System With Popularity and Association Rule

    • What Is the Definition of Popularity and What Is Support

    • How to Encode an Item-Order Matrix

    • What Are Confidence and Lift

    • What Is Association Rule and How to Apply Apriori Algorithm

    • How to Evaluate Results With Selected Criteria

  • End-Of-Course Conclusion

Who this course is for:

Course Details:

  • 1 hour on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

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Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fastai and Python

Demo Link: https://www.udemy.com/course/recommender-system-with-machine-learning-and-statistics/

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