Building Recommendation System Using MXNET on AWS Sagemaker from Coursera

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project for training the model, and if you don't have access to this instance type, please contact AWS support and request access.

In this 2-hour long project-based course, you will how to train and deploy a Recommendation System using AWS Sagemaker. We will go through the detailed step by step process of training a recommendation system on the Amazon's Electronics dataset. We will be using a Notebook Instance to build our training model. You will learn how to use Apache's MXNET Deep Learning Model on the AWS Sagemaker platform.

Since this is a practical, project-based course, we will not dive in the theory behind recommendation systems, but will focus purely on training and deploying a model with AWS Sagemaker. You will also need to have some experience with Amazon Web Services (AWS) and knowledge of how deep learning frameworks work.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

What's inside

Syllabus

Project Overview

Welcome to this Guided Project on How to Build a Recommendation System Using MXNET on AWS Sagemaker. In this project, you will learn the step by step process of how to train and deploy a recommendation system on AWS Sagemaker using the Deep Learning Framework Apache MXNET. Will start with creating a Sagemaker Notebook Instance which will be used for executing the entire coding process. We will first download the Dataset which will we are using (Amazon's Electronics Review Dataset) followed by exploring the data. Then will create the required functions for preparing, training and executing the model and finally deploy the model to production and evaluate it.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Intended for those new to deep learning and recommendation systems on AWS

Teaches learners how to use Apache's MXNET Deep Learning Model

Suitable for learners with some experience with AWS and deep learning frameworks

Involves a practical project-based approach to learning

Requires a working knowledge of Python

Recommended for learners based in the North America region

Reviews summary

Mxnet course falls short

According to students, this course features difficult to understand lectures and vague explanations. The course has low-quality projects which skip over important details and focus solely on code. There is also a lack of theory in this course as well as unengaging assignments.

Assignments in this course fail to engage students.

Students feel that the course lacks theory.

"It is ok not to go through theory as it was mentioned in the beginning of the project."

Many students found that the lectures were difficult to understand.

"It was difficult to understand the speaker most of the time."

"The details of what was being implemented were skipped."

Projects in this course are not well received by students and lack detail.

"Loved the project."

"It is ok not to go through theory as it was mentioned in the beginning of the project. But the instructor just goes through the code and either does not explain or explains poorly. Very low quality project."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Recommendation System Using MXNET on AWS Sagemaker with these activities:

Organize course resources

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Get organized by compiling and reviewing course materials, such as notes, assignments, and quizzes, to enhance your retention and improve your learning outcomes.

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Gather all course materials
Create a system for organizing and storing materials

Review Python programming

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Brush up on Python programming fundamentals to strengthen your foundation before starting the course, ensuring a smoother learning experience.

Browse courses on Python Programming

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Go through Python tutorials or documentation
Solve practice problems or coding challenges

Tutorial on Apache's MXNET for Deep Learning

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Deepen your understanding of Apache's MXNET for deep learning by exploring hands-on tutorials, improving your proficiency in using this framework.

Browse courses on Deep Learning

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Find a comprehensive tutorial on Apache's MXNET
Follow the tutorial step-by-step

Four other activities

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Tutorial on AWS Sagemaker

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Enhance your understanding of AWS Sagemaker by exploring hands-on tutorials, gaining practical experience in using this cloud platform.

Browse courses on AWS SageMaker

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Find a comprehensive tutorial on AWS Sagemaker
Follow the tutorial step-by-step

Join a study group

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Collaborate with peers by joining a study group to discuss course concepts, share insights, and reinforce your understanding through group learning.

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Find or create a study group with other students
Meet regularly to discuss course materials and assignments

Practice model building with MXNET

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Practice building recommendation models using MXNET to master the techniques and improve your hands-on experience.

Browse courses on MXnet

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Create a sample dataset
Build a simple recommendation model using MXNET
Train and evaluate the model

Develop a case study

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Apply your knowledge by developing a case study that showcases your understanding of recommendation systems and your ability to apply them to real-world scenarios.

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Identify a business problem that can be solved using a recommendation system
Design and implement a recommendation system solution
Evaluate the performance of your recommendation system
Write a case study report summarizing your findings

Career center

Learners who complete Building Recommendation System Using MXNET on AWS Sagemaker will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist uses statistical examination and data analysis to extract meaningful insights from structured and unstructured data. This course will be very helpful for Data Scientists because it provides a foundational understanding of building a recommendation system using MXNET and AWS Sagemaker. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Data Scientists develop the skills they need to build and deploy effective recommendation systems that can be used in a variety of applications, such as e-commerce, streaming media, and personalized learning.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models to solve real-world problems. This course will be helpful for Machine Learning Engineers because it provides a practical understanding of building a recommendation system using MXNET and AWS Sagemaker. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Machine Learning Engineers develop the skills they need to build and deploy effective recommendation systems that can be used in a variety of applications.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course will be helpful for Software Engineers because it provides a practical understanding of building a recommendation system using MXNET and AWS Sagemaker. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Software Engineers develop the skills they need to build and deploy effective recommendation systems that can be used in a variety of applications.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst collects, analyzes, interprets, and presents data to help businesses make informed decisions. This course will be helpful for Data Analysts because it provides a foundation in building a recommendation system using MXNET and AWS Sagemaker. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Data Analysts develop the skills they need to build and deploy effective recommendation systems that can be used in a variety of applications, such as marketing, sales, and customer service.

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Product Manager

A Product Manager develops and manages product roadmaps, working with engineers and designers to bring products to market. This course will be helpful for Product Managers because it provides an understanding of the technical aspects of building a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Product Managers make informed decisions about the features and functionality of their products.

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Business Analyst

A Business Analyst analyzes business processes and systems to identify areas for improvement. This course will be helpful for Business Analysts because it provides an understanding of the technical aspects of building a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Business Analysts understand how recommendation systems can be used to improve business outcomes.

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Marketing Manager

A Marketing Manager develops and executes marketing campaigns to promote products and services. This course will be helpful for Marketing Managers because it provides an understanding of the technical aspects of building a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Marketing Managers understand how recommendation systems can be used to improve marketing campaigns.

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Sales Manager

A Sales Manager leads and motivates a team of sales professionals to achieve sales goals. This course will be helpful for Sales Managers because it provides an understanding of the technical aspects of building a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Sales Managers understand how recommendation systems can be used to improve sales performance.

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Customer Success Manager

A Customer Success Manager ensures that customers are satisfied with their products and services. This course will be helpful for Customer Success Managers because it provides an understanding of the technical aspects of building a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Customer Success Managers understand how recommendation systems can be used to improve customer satisfaction.

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Technical Writer

A Technical Writer creates and maintains technical documentation, such as user manuals and whitepapers. This course may be helpful for Technical Writers because it provides an overview of the process of building and deploying a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Technical Writers understand the technical aspects of recommendation systems and how they can be used to improve user experience.

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User Experience Designer

A User Experience Designer researches, designs, and evaluates user interfaces to ensure that they are easy to use and enjoyable. This course may be helpful for User Experience Designers because it provides an overview of the process of building and deploying a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help User Experience Designers understand the technical aspects of recommendation systems and how they can be used to improve user experience.

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Data Engineer

A Data Engineer builds and maintains the infrastructure that is used to store and process data. This course may be helpful for Data Engineers because it provides an overview of the process of building and deploying a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Data Engineers understand the technical aspects of recommendation systems and how they can be used to improve data processing.

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Database Administrator

A Database Administrator manages and maintains databases. This course may be helpful for Database Administrators because it provides an overview of the process of building and deploying a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Database Administrators understand the technical aspects of recommendation systems and how they can be used to improve database performance.

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Systems Administrator

A Systems Administrator manages and maintains computer systems and networks. This course may be helpful for Systems Administrators because it provides an overview of the process of building and deploying a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Systems Administrators understand the technical aspects of recommendation systems and how they can be used to improve system performance.

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Network Engineer

A Network Engineer designs, builds, and maintains computer networks. This course may be helpful for Network Engineers because it provides an overview of the process of building and deploying a recommendation system. The course covers the entire process of training and deploying a recommendation system, from data preparation to model evaluation. It will help Network Engineers understand the technical aspects of recommendation systems and how they can be used to improve network performance.

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