Recommendation Systems With Terraform On Google Cloud

[BaDshaH]

Published 6/2024
Created by Minerva Singh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 51 Lectures ( 3h 14m ) | Size: 1.55 GB

Use the Power of Google Cloud Computing To Build Sate-of-Art Recommender Systems

What you'll learn:
Introduction to getting started with Google Cloud Platform (GCP)
Reading and processing data within GCP
Introduction to Terraform
Develop recommender systems

Requirements:
Should have prior experience of Python data science
Prior experience of statistical and machine learning techniques will be beneficial
Should have an interest in cloud environments
Prior exposure to recommender systems

Description:
Recommendation Systems With Terraform On Google Cloud: Use the Power of Google Cloud Computing To Build State-of-the-Art Recommender SystemsUnlock the potential of personalized user experiences and drive engagement with this comprehensive course on building state-of-the-art recommender systems using Google Cloud Platform (GCP) and Terraform.Course OverviewThis course will equip you with the knowledge and tools to design, deploy, and manage powerful recommendation engines that can be scaled to meet the demands of modern applications. You'll learn how to leverage the vast capabilities of GCP's infrastructure and machine learning services, combined with the automation and scalability offered by Terraform.What You'll LearnIntroduction to the GCP Ecosystem: Learn about the core components of GCP relevant to recommender systems, including Compute Engine, Cloud Storage, BigQuery, and Vertex AI.Essential Statistical Concepts: Master fundamental statistical techniques, such as Principal Component Analysis (PCA), which are crucial for understanding and implementing recommender algorithms.Common Recommender Systems: Explore a variety of popular recommendation approaches, including collaborative filtering, content-based filtering, and hybrid models.Filtering-Based Recommender Systems: Dive deep into the mechanics of filtering-based recommenders, understanding how they leverage user-item interactions to generate personalized suggestions.Other Recommender Systems: Discover additional recommendation techniques, such as knowledge-based and session-based systems, expanding your toolkit for diverse scenarios.Getting Started with Terraform: Learn the basics of Terraform, a powerful infrastructure-as-code tool, and apply it to automate the deployment and management of your recommender systems on GCP.Text Analysis for Recommendations: Gain insights into text analysis techniques (e.g., NLP) and how they can be integrated into recommender systems to leverage textual data for improved recommendations.Who This Course Is ForThis course is designed forata scientists and machine learning engineers interested in building and deploying recommender systems.Software developers and DevOps professionals seek to automate infrastructure provisioning for recommendation engines on GCP.Business analysts and product managers who want to understand the technical aspects of recommender systems to make informed decisions.PrerequisitesBasic understanding of Python programming.Familiarity with machine learning concepts is beneficial but not required.By the end of this course, you will be able to:Confidently designed and implemented various recommender system algorithms.Leverage GCP's infrastructure and machine learning services for scalable recommendation engines.Automate the deployment and management of recommender systems using Terraform.Incorporate text analysis techniques to enhance the personalization of recommendations.Enrol now and start your journey toward building cutting-edge recommender systems on Google Cloud!

Who this course is for:
People wanting to harness the power of cloud computing via GCP
Learn powerful GCP related technologies, including BigQuery and AutoML
People wanting to implement and deploy machine learning models in GCP
People wanting to learn to make data-driven recommendations
People looking to start with Terraform

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