Welcome to an introduction to using artificial intelligence and machine learning with a focus on Amazon Web Services and the Google Cloud Platform. This class is designed to be a gentle introduction, which means we'll be starting at the ground up and focusing on giving students the tools and materials they need to navigate this space. As this is an introduction, we will also briefly touch on the necessary skills required to leverage artificial intelligence and machine learning by briefly discussing data engineering, cloud management, systems engineering, and even some release engineering and best practices around models and the management of them on the cloud.

By the end of this course, you'll hopefully understand how to take more advanced courses from Cloud Academy, and even have a springboard to handle complex tasks in your day-to-day jobs. The goal is to provide you useful, relevant information, whether it be to a professional, a student, or a hobbyist who's just tinkering. Also, there are several labs (Machine Learning - Training Custom Models & Testing Your Models in the Real World) directly tied to this learning path, which will provide hands-on experience to supplement the academic knowledge provided in the lectures.

This course is part of a multi-series learning path, ideal for those who are interested in understanding machine learning from a 101 perspective. If you already have a familiarity with machine learning concepts, such as how a model, data and results relate, you may wish to skip ahead to module two, especially if you're already familiar with the basics of training and inferencing a model.

There are more advanced labs as part of this learning path, and you're more than ready to skip ahead and will not be missing much if you're already familiar with those concepts. It also helps if you have some light data engineering or developer background, as several parts of this class, particularly the labs, involve hands-on work and manipulating basic data structures and scripts. The labs all have highly detailed notes to help novice users understand them, but you'll be more easily able to expand the knowledge at your own pace with a good baseline understanding.

Most importantly, this class is designed for those who want entry-level experiencing, starting from a very basic level and ramping up over time. As we explain the core concepts, there are some prerequisites from the course, but there aren't many. It's recommended that you have a basic familiarity with one of the cloud providers, especially AWS or GCP. Azure, Oracle and the other providers also have machine learning suites that you could follow along with this class, but this class will focus on examples from those two in particular.

Also, specifically if you have an interest in completing the labs and gaining some hands-on experience, Python is a helpful language to understand. Now, from the broader perspective if you're looking to a career in machine learning, you can absolutely do it with languages such as Java, C-Sharp, even lower level languages such as C++, or functional languages such as R or Matlab.

However, in my experience, Python is the most widely adopted language, specifically if you're looking to go into heavy duty training, learning and model development. Furthermore, Python has the added benefit of being able to work with most cloud platforms for other tasks such as infrastructure automation. However, Java and Scala are absolutely strong options as well.

And finally, before we get started, let me introduce myself. I am Chris Gambino, one of the architects and founders at Calculated Systems. Before joining Calculated Systems, I was a big data engineer for Google who specialized in projects involving massive amounts of data, such as streaming and enriching data for media companies, and even experimental projects such as building IoT-connected cars.

Lectures

• Hierarchy
• Levels of AI Users
• Level 1: Leveraging Out of the Box
• Case Study