Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
Module 1: Introduction
-Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.
Module 2: Identifying business value for using ML
-This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.
Module 3: Defining ML as a practice
-There are several types of machine learning problems. In this module, you'll learn to differentiate between the most common ones; develop the key vocabulary to support yourself when working with ML experts; practice categorizing various examples of ML problems; and identify the short- and long-term benefits when solving those ML problems.
Module 4: Building and evaluating ML models
-After you have assessed the feasibility of your supervised ML problem, you're ready to move to the next phase of an ML project. This module explores the various considerations and requirements for building a complete dataset in preparation for training, evaluating, and deploying an ML model. It also includes two demos—Vision API and AutoML Vision—as relevant tools that you can easily access yourself or in partnership with a data scientist. You'll also have the opportunity to try out AutoML Vision with the first hands-on lab.
Module 5: Using ML responsibly and ethically
-Data in the world is inherently biased, and that bias can be amplified through ML solutions. In this module, you'll learn about some of the most common biases and how they can disproportionately affect or harm an individual or groups of individuals. You'll also be given guidelines for uncovering possible biases at each phase of an ML project and strategies for achieving ML fairness as much as possible.
Module 6: Discovering ML use cases in day-to-day business
-This module explores 5 general themes for discovering ML use cases within day-to-day business, followed by concrete customer examples. You'll learn about creative applications of ML, such as improving the resolution of images or generating music.
Module 7: Managing ML projects successfully
-When you thoroughly understand the fundamentals of machine learning and considerations within in each phase of the project, you're ready to learn about the best practices for managing an ML project. This module describes 5 key considerations for successfully managing an ML project end-to-end: identifying the business value, developing a data strategy, establishing data governance, building successful ML teams, and enabling a culture of innovation. You'll also have an opportunity to gain further exposure to one of Google Cloud's tools by completing a final hands-on lab: Evaluate an ML Model with BigQuery ML.
Module 8: Summary
-This module provides a summary of the key points covered in each of the modules in the course.
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Anonymous completed this course.
Although the course is all about machine learning, since it's a non-technical course, I strongly recommend for those who want to take a look at how ML engineers are doing and see if they fit in this career. GG designs the course precisely and one unique thing is their Qwiklabs exercises to give students real life experiment of ML. Quizzes are short and easy, which are good points.
Rajnish Pandey is taking this course right now, spending 4 hours a week on it and found the course difficulty to be medium.
This is very good website to learn .I recommend to all bigners to learn and develop our skill through it's website
So this is amazing to learn and have many course to learn absolutely free for some course also.
Anonymous completed this course.
Google Cloud Hosting, and or Compute Engine, is just one of many services provided by Google Cloud Platform. They also offer their App Engine, storage, DNS, CDN, and a multitude of other services and products. Google Cloud Platform consists of a set of...
Google Cloud Hosting, and or Compute Engine, is just one of many services provided by Google Cloud Platform. They also offer their App Engine, storage, DNS, CDN, and a multitude of other services and products. Google Cloud Platform consists of a set of physical servers, as well as virtual resources like virtual machines (VMs) that are contained in Google’s data centers around the globe. We use Google Cloud Platform’s multi-regional deployment mode. This means that you can choose a different data center for each of your WordPress websites.
Some notable companies that are using Google Cloud Platform (Compute Engine or App Engine) include Spotify, HSBC, Home Depot, Snapchat, HTC, Best Buy, Philips, Coca Cola, Domino’s, Feedly, ShareThis, Sony Music, and Ubisoft. Even Apple has been seen moving some of its services away from AWS, spending between $400 million and $600 million on Google Cloud Platform; although this couldn’t be independently confirmed.
IFKIREN Aissam is taking this course right now.
Bonjour, je suis un étudiant en premier année master, et j'ai j'ai très besoin de ce cours là pour me développer à la coté de datascience et Machine Learning, et j'ai remarqué que ce cours est de bonne qualité et aussi il me permet de me bien développée dans cette coté et de me permet de partir très loin dans ce sens là.