Course Overview
In this course, you construct an ML pipeline that serves as a recommendation engine by using your understanding of classification models and embeddings.
The fifth and last lesson in the Advanced Machine Learning on Google Cloud series is this one
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Prerequisites
- Prior familiarity with foundational machine learning concepts as covered in Machine Learning on Google Cloud.
- Familiarity with cloud concepts and fundamentals, networking, and security.
- Basic proficiency with a scripting language like Python, as covered in the Google Python Crash course.
- Basic proficiency with SQL.
- Familiarity with Python and TensorFlow.
Audience Profile
Aspiring machine learning data analysts, data scientists, data engineers, and programmers interested in applying machine learning to recommendation systems in practice.
Learning Objectives
- Create a recommendation engine that is content-based.
- Implement a recommendation engine with collaborative filtering.
- Create a hybrid recommendation engine with embeddings for users and content.
- In the context of suggestions, use reinforcement learning techniques for contextual bandits.
Content Outline
This module outlines the topics to be covered in the course.
In this module, recommendation systems are defined, various types of recommendation systems are reviewed, and frequent issues that occur when designing recommendation systems are covered.
This lesson teaches how to use Qwiklabs to finish each of your labs using Google Cloud and how to develop a recommendation system utilising the users' and objects' characteristics.
This module shows how the data of the interactions between users and items from many different users can be combined to improve the quality of predictions.
This module shows how various recommendation systems can be combined as part of a hybrid approach.
This module presents the goals of reinforcement learning and shows where reinforcement learning fits in machine learning.
This module reviews the topics explored in this course.
FAQs
A: A recommender, also known as a recommendation system, is a type of information filtering system that aims to anticipate the "rating" or "preferred" that a user will assign to a certain item.
- The personalized one (relevant to that user)
- The diverse one (includes different user interests)
- The one that doesn't recommend the same items to users for the second time
- The one that recommends available products
A: The Recommendations page automatically generates recommendations that could enhance your performance based on the performance history of your account, campaign settings, and trends throughout Google.
A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are on board the company.
Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.
Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.
Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts at any time from your respective faculty.
Our trainers have the patience and ability to explain complex concepts simplistically with depth and width of knowledge.
To ensure quality learning, we provide support sessions even after the training program.
A: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
A: We recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
A: Radiant Techlearning has a data center containing a Virtual Training environment for participants' hand-on-practice.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere and in any time zone.
A: The learners will be enthralled as we engage them in the natural world and Oriented industry projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you a lot in your future tasks and assignments.