Building Practical Recommendation Engines – Part 2
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Building Practical Recommendation Engines – Part 2

Use behavioral and historical data to predict the future
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
3 students enrolled
Created by Packt Publishing
Last updated 2/2017
English
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Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 2 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Build your first recommendation engine
  • 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
View Curriculum
Requirements
  • To work along this course you'll need to have a basic understanding of recommendation engines using R. Familiarity with Python, Spark, Neo4j and Hadoop would be beneficial.
  • The software requirements of the course are:
  • Anaconda 4.2 for Python 3.5
  • Neo4j 3.0.6
  • Spark 2.0
  • Hadoop 2.5 -Mahout 0.12
  • Java 7/Java 8
  • WINDOWS 7+/Centos 6
Description

Recommendation systems allow you to gain insights into data and make a guess on what would be people's preference. It is used all over the web, be it shopping, social networking, or music. This video will teach you how to build unique end-to-end recommendation engines with various tools and enhance your skills.

You will look at various 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 the video. During the course of the video, you will come across creating recommendation engines with R, Python, Apache Spark, Neo4j, Apache Mahout, and more. By the end of the course, you will also learn the best practices and tricks and tips to build efficient recommender systems.

About The Author

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, 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 is the target audience?
  • This video is intended to data analysts, data science experts, or anyone who knows th basics of recommendation engines, and are looking to build advanced recommendation engines and upskill themselves.
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Curriculum For This Course
29 Lectures
02:12:15
+
Building Personalized Recommendation Engines
5 Lectures 28:13

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
10:21

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

Content-Based Recommendation Using Python
08:15

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

Context-Aware Recommender Systems
02:23

Create context profile of the user. 

Creating Context Profile
04:12
+
Building Real-Time Recommendation Engines with Spark
7 Lectures 38:07

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
03:32

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

Setting Up Spark
05:11

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

Collaborative Filtering Using Alternating Least Square
03:34

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

Model Based Recommender System Using pyspark
02:18

Know the actual implementation of the recommendation engine.

The Recommendation Engine Approach
09:24

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

Model Evaluation and Selection with Hyper Parameter Tuning
10:25
+
Recommendation with Neo4j
9 Lectures 28:17

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

Preview 07:07

Learn the cypher query language.

Neo4j
03:23

Learn to create nodes and relationships.

Building Your First Graph
04:00

Learn how to install Neo4j for Windows.

Neo4j Windows Installation
01:06

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

Installing Neo4j on the Linux Platform
01:48

Build recommendation engines using the interface.

Building Recommendation Engines
03:04

Learn to write a query to generate recommendation.

Generating Recommendations Using Neo4j
01:51

Ability to implement collaborative filtering using Euclidean distance method.

Collaborative filtering Using the Euclidean Distance
03:38

Ability to implement collaborative filtering using cosine similarity.

Collaborative Filtering Using Cosine Similarity
02:20
+
Building Scalable Recommendation Engines with Mahout
5 Lectures 23:03

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
10:16

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

Item-Based Collaborative Filtering
02:50

Evaluate the accuracy of the recommender models that we built.

Evaluating Collaborative Filtering with User-Item Based Recommenders
03:41

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

SVD Recommenders
01:55
+
The Future of Recommendation Engines
3 Lectures 14:35

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
01:57

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

Popular Methodologies
04:46
About the Instructor
Packt Publishing
3.9 Average rating
7,349 Reviews
52,590 Students
616 Courses
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