Machine learning is a hot topic and believe it or not, you don't need to be an engineer or mathematician to know how it works. In fact, if you're a business professional in a nontechnical role, this course is especially for you. Hi, my name is Saman Javan, Lead Business Curriculum Developer at Google Cloud. And this is Managing Machine Learning Projects with Google Cloud, formerly known as ML for business professionals. As with all of our business courses, I've worked with some of the most brilliant minds at Google to bring you new content and an improved learning experience. For this course in particular, I've worked with Priscilla Moraes who leads our portfolio of machine learning courses, and Barry Schmell, one of our lead instructors for GCP and GSuite. Now, let's dive right into what the course covers. We're in the course introduction right now, so let's move to what's covered in module 2. In the second module, we'll explore the business value for using machine learning. This is the why we're using machine learning in business. Next, we'll define machine learning as a practice. This module will provide the foundational vocabulary which is important, especially if you're planning on working with machine learning experts. It will also give you a framework for breaking down an ML project. In module 4, we'll look at how to build and evaluate ML models. At this point, it will become clear that data plays a central role in your ability to successfully use ML. We'll shift briefly in module 5 to discuss what it means to use ML responsibly and ethically. In module 6, we'll return to everyday business where I'll teach you some common ways to discover ML use cases. These methods will be applicable no matter what industry you're working in. In module 7, I'll explain holistically how to manage ML projects successfully. And finally, we'll close the course by offering additional resources for those of you who might be interested in continuing your learning beyond this course. By the end of this course, I want you to gain a thorough understanding of how machine learning can be used to improve business processes and create new value. We'll explores several examples of where machine learning has been used by other businesses, assess various ML use cases for their feasibility. You'll learn throughout the course that not all ideas are solvable or ideal for ML. Next, I want you to learn about the end-to-end process to carry out an ML project, including building, training and evaluating an ML model. As part of the end-to-end process, you'LL learn how to define data characteristics and biases that affect the quality of ML models. You'll learn to recognize key considerations for managing ML projects, including data strategy, governance and project teams. And finally, you'll be able to put everything you've learned to create a custom ML use case that can meaningfully impact your business. Let me also assure you of what we won't be covering. We won't be building an actual machine learning model along with the associated cost plan. This is not a programming class, so you're not expected to know how to code, do complex math or do analysis. This is also not an in-depth course on Tensorflow that we do have some great training on that if you're interested. Be sure to check out our training catalog at cloud.google.com/training. We also won't be teaching you how to build data pipelines or select specific algorithms for your ML use case. Ultimately, the goal of this course is to teach you enough about what machine learning is and the steps in managing a machine learning project, so that you can work with a machine learning practitioner. This is a non technical course. What I want you to walk away with is knowing where to apply ML in your organization so that you can get the best return on your investment. To support your learning, this course comes with several materials. You'll find a PDF file containing a subset of slides used in this course. I say subset because there are hundreds of slides and we've reduced them down to the key points in the course. There are also greeted assessment questions per module. We've provided worksheets for some of the modules in the course. These are complementary to the training content and will help you to identify your own ML use case. We also want you to be able to practice using a few tools that will help you gain a more in-depth understanding of how machine learning works and where it could be valuable in a business. You'll have an opportunity to use these tools with the hands-on-labs. We'll cover the requirements to use the hands-on-lab later in the course when we first introduced them. And with that, you're ready to begin the next module.
