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Learning Path: Build Your Own Recommendation Engines

Video Description

Predict the future by making the most of the data you have today!

In Detail

With the progress in time, we do not have to rely on crystal balls any more to predict the future, we have data! Recommender systems or Recommendation Engines serve as the modern-day crystal balls, with the exception that all of the predictions made by them are backed by data! Recommendation Engines are very common these days and can be applied in a variety of applications.

In this Learning Path, you will be introduced to what a recommendation engine is, its applications. You will then learn to build recommender systems by using popular frameworks such as R, and Python.

The later part of the Learning Path, will deal with various complex recommendation engines such as personalized recommendation engines, real-time recommendation engines, SVD recommender systems. You will also get a quick glance into the future of recommendation systems.

By the end of this Learning Path, you will be able to build efficient recommendation engines by following the best practices.

Prerequisites: The knowledge of the data science concepts is beneficial.

Resources: Code downloads and errata:

  • Building Practical Recommendation Engines – Part 1

  • Building Practical Recommendation Engines – Part 2

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Building Practical Recommendation Engines – Part 1 (2h 52m)

  • Building Practical Recommendation Engines – Part 2 (2h 12m)

  • Table of Contents

    1. Chapter 1 : Building Practical Recommendation Engines – Part 1
      1. The Course Overview 00:04:36
      2. Recommendation engine definition 00:04:13
      3. Types of recommender systems 00:05:19
      4. Evolution of recommender systems with technology 00:05:45
      5. Loading and formatting data 00:06:04
      6. Calculating similarity between users 00:01:52
      7. Predicting the unknown ratings for users 00:07:43
      8. Nearest neighborhood-based recommendation engines 00:08:15
      9. Content-based recommender system 00:04:51
      10. Context-aware recommender system 00:03:14
      11. Hybrid recommender systems 00:02:48
      12. Model-based recommender systems 00:03:31
      13. Neighborhood-based techniques 00:10:36
      14. Mathematical model techniques 00:11:50
      15. Machine learning techniques 00:02:47
      16. Classification models 00:18:47
      17. Clustering techniques and dimensionality reduction 00:07:57
      18. Vector space models 00:07:22
      19. Evaluation techniques 00:09:02
      20. Installing the recommenderlab package in RStudio 00:01:31
      21. Datasets available in the recommenderlab package 00:03:14
      22. Exploring the dataset andbuilding user-based collaborative filtering 00:17:33
      23. Building an item-based recommender model 00:10:40
      24. Collaborative filtering using Python 00:02:11
      25. Data exploration 00:05:38
      26. User-based collaborative filtering with the k-nearest neighbors 00:02:36
      27. Item-based recommendations 00:02:56
    2. Chapter 2 : Building Practical Recommendation Engines – Part 2
      1. The Course Overview 00:03:03
      2. Personalized and Content-Based Recommender System 00:10:22
      3. Content-Based Recommendation Using Python 00:08:16
      4. Context-Aware Recommender Systems 00:02:23
      5. Creating Context Profile 00:04:12
      6. About Spark 2.0 00:03:44
      7. Spark Core 00:03:33
      8. Setting Up Spark 00:05:12
      9. Collaborative Filtering Using Alternating Least Square 00:03:34
      10. Model Based Recommender System Using pyspark 00:02:19
      11. The Recommendation Engine Approach 00:09:24
      12. Model Evaluation and Selection with Hyper Parameter Tuning 00:10:26
      13. Discerning Different Graph Databases 00:07:08
      14. Neo4j 00:03:23
      15. Building Your First Graph 00:04:01
      16. Neo4j Windows Installation 00:01:07
      17. Installing Neo4j on the Linux Platform 00:01:48
      18. Building Recommendation Engines 00:03:05
      19. Generating Recommendations Using Neo4j 00:01:52
      20. Collaborative filtering Using the Euclidean Distance 00:03:38
      21. Collaborative Filtering Using Cosine Similarity 00:02:20
      22. Setting up Mahout with General Introduction 00:04:21
      23. Core Building Blocks of Mahout 00:10:16
      24. Item-Based Collaborative Filtering 00:02:50
      25. Evaluating Collaborative Filtering with User-Item Based Recommenders 00:03:41
      26. SVD Recommenders 00:01:55
      27. Future and Phases of Recommendation Engines 00:07:52
      28. Using Cases to Look Out for 00:01:58
      29. Popular Methodologies 00:04:47