Learning Path: Build Your Own Recommendation Engines
3.1 (67 ratings)
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Learning Path: Build Your Own Recommendation Engines

Make valuable future-ready decisions that are backed by data using modern recommender systems
3.1 (67 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
325 students enrolled
Created by Packt Publishing
Last updated 9/2018
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Discover the tools needed to build recommendation engines
  • Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations
  • Familiarize yourself with machine learning algorithms in different frameworks
  • Master different versions of recommendation engines from practical code examples
  • Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others
Course content
Expand all 56 lectures 05:04:56
+ Building Practical Recommendation Engines – Part 1
27 lectures 02:52:41

This video will brief introduction about the entire course

Preview 04:36

Learn to provide relevant suggestions based on datamining approaches

Recommendation engine definition

know about the different types of popular recommender systems and its use

Types of recommender systems

Learn to use the large user base and products which can scale easily and respond fast

Evolution of recommender systems with technology

Learn to install R Package in Rstudio to know how to load and format data

Preview 06:04

Learn to use the correlation value as the measure of similarity between two items in a matrix.

Calculating similarity between users

Learn to predict the unrated movies of a specified user using the ratings given by similar users

Predicting the unknown ratings for users

Learn to develop new and different approaches

Preview 08:14

Learn to extract the features that represent the product

Content-based recommender system

Learn to capture the context information of the user and refine their suggestions accordingly.

Context-aware recommender system

Learn to implement the hybrid recommendation engine based on the problem statement and business needs

Hybrid recommender systems

Learn to build a probability modelusing the prior probabilities from the available data

Model-based recommender systems

Learn to know the simple mathematical calculation that is applied between two vectors

Preview 10:35

Know about matrix factorization, alternate least squares, and singular value decomposition

Mathematical model techniques

Learn to predict the future outcomes based on given input parameters

Machine learning techniques

Learn linear classification, KNN classification, support vector machines, decision trees, and various ensemble methods

Classification models

Learn to know the process of grouping objects in one Group

Clustering techniques and dimensionality reduction

Learn to compute the similarity measure metric that data should be all numeric

Vector space models

Learn about cross validation and various popular evaluation metrics like root mean square error

Evaluation techniques

The recommenderlab R package is a framework for developing and testing recommendation algorithms used to build recommendation engines. In this video, we’ll see how to installrecommenderlab

Preview 01:31

Like any other package available in R, recommenderlab also comes with default datasets. We need to know how to see available packages, methods, and algorithms. 

Datasets available in the recommenderlab package

Now that we have started with the basics of data set, we must explore the data in more detail. This video will do exactly that and guide us in building and evaluating a recommender model

Exploring the dataset andbuilding user-based collaborative filtering

We use the same Jester5k dataset for the item-based recommender system as with UBCF. But, removing data of certain users and building IBCF system can be a bit tricky. Let’s see how we do that

Building an item-based recommender model

Now, we have seen implementations of user-based recommender systems and item-based recommender systems using the R package, recommenderlab. But, can we do the same with Python? In this video, we see theUBCF and IBCF implementations using the Python programming language 

Collaborative filtering using Python

Let’s explore the MovieLens dataset and also prepare the data required for building collaborative filtering recommendation engines using python

Data exploration

If we observe the RMSE values in the model, we can see that the error is a bit higher. The reason may be that we have chosen all the users' rating information while making the predictions. In this video, we will cover finding the top-N nearest neighbors. 

User-based collaborative filtering with the k-nearest neighbors

IBCF is very similar to UBCF but with very minor changes in how we use the rating matrix. But, still we must learn how to evaluate the model. This video guides in evaluating the model.

Item-based recommendations
+ Building Practical Recommendation Engines – Part 2
29 lectures 02:12:15

This video provides an overview of the entire course.

Preview 03:02

Learn to know the flavors of personalized recommenders.

Personalized and Content-Based Recommender System

Build content recommendations using another approach, using the Python sklearn, NumPy, and pandas packages.

Content-Based Recommendation Using Python

Use different recommendations to the same person based on their current context.

Context-Aware Recommender Systems

Create context profile of the user.

Creating Context Profile

Learn the basics of spark and its architectures.

Preview 03:43

Learn to provide the capabilities of Spark, such as in-memory distributed computation and fast, easy-to-use APIs. 

Spark Core

Learn to build a specific version of Hadoop to access HDFS as well as standard and custom Hadoop input sources.

Setting Up Spark

Know the Matrix Factorization Model and the Alternating Least Squares method.

Collaborative Filtering Using Alternating Least Square

Learn to build the recommendation engine using Sparksuch as DataFrames, RDD, Pipelines, and Transforms available in Spark MLlib.

Model Based Recommender System Using pyspark

Know the actual implementation of the recommendation engine.

The Recommendation Engine Approach

Learn to choose the Root Mean Squared Error method to calculate the model accuracy.

Model Evaluation and Selection with Hyper Parameter Tuning

Learn to understand the concept of databases and where to apply them.

Preview 07:07

Learn the cypher query language.


Learn to create nodes and relationships.

Building Your First Graph

Learn how to install Neo4j for Windows.

Neo4j Windows Installation

Learn to download and install Neo4j on the CentOS Linux Platform.

Installing Neo4j on the Linux Platform

Build recommendation engines using the interface.

Building Recommendation Engines

Learn to write a query to generate recommendation.

Generating Recommendations Using Neo4j

Ability to implement collaborative filtering using Euclidean distance method.

Collaborative filtering Using the Euclidean Distance

Ability to implement collaborative filtering using cosine similarity.

Collaborative Filtering Using Cosine Similarity

Learn to setup the Apache mahout software.

Preview 04:21

Build customized recommender systems that are enterprise-ready, scalable, flexible, and that perform well.

Core Building Blocks of Mahout

Item-based recommenders recommend similar items to users by considering the similarity between items instead of the similarity between users.

Item-Based Collaborative Filtering

Evaluate the accuracy of the recommender models that we built.

Evaluating Collaborative Filtering with User-Item Based Recommenders

Use of matrix factorization methods to generate model-based recommender implementations in Mahout.

SVD Recommenders

Understand the directions in which recommendation engines are evolving to cope with futuristic situations.

Preview 07:52

List a few promising use cases that might make you more interested in future of recommendation engines.

Using Cases to Look Out for

Learn to build recommendation engines for improving the robustness and relevance of the recommendations.

Popular Methodologies
  • You are required to know basics of data manipulation languages such as Python, R, or similar languages.

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!

Recommender Systems also perform the task of filtering, prioritizing and efficiently delivering relevant information in order to alleviate the problem of information overload, which has created a potential problem to many users.

With all these advantages, Recommendation Engines are very common these days and can be applied in almost every field.

Packt’s Video Learning Paths are an amalgamation of multiple video courses that are logically tied together to provide you with a larger learning curve.

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

The latter part of the Learning Path will deal with various complex recommendation engines such as personalized recommendation engines, real-time recommendation engines, and 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.

For this Learning Path, we have taken two video courses both authored by Suresh Kumar Gorakala.

Suresh Kumar Gorakala is a Data scientist focused on Artificial Intelligence. He has professional experience close to 10 years, having worked with various global clients across multiple domains and helped them in solving their business problems using Advanced Big Data Analytics. He has extensively worked on Recommendation Engines, Natural language Processing, Advanced Machine Learning, and Graph Databases. He previously co-authored Building a Recommendation System with R for Packt Publishing. He is a passionate traveler and is a photographer by hobby.

Who this course is for:
  • This Learning Path is for anyone who is new to the field of data science and has a basic knowledge of data manipulation languages such as R or Python.