Introduction to Machine Learning - Part One
Introduction to Machine Learning
Welcome to an introduction to using Artificial Intelligence and Machine Learning with a focus on Amazon Web services and the Google Cloud platform. This course is designed to be a gentle introduction, starting at the ground up and focusing on giving students the tools and materials they need to navigate the topic. It will also include the necessary skills around data engineering, cloud management and even some systems engineering. There are several labs directly tied to this learning path, which will provide hands-on experience to supplement the academic knowledge provided in the lectures.
This course begins with an introduction to AI and ML, before moving onto explain the different levels of users in the field. Then we take a look at out-of-the-box solutions for AI and ML, before looking at a case study to give you the topics covered during this course in a real-world example.
For any feedback relating to this course, please contact us at email@example.com.
By the end of this course, you'll hopefully understand how to take more advanced courses and even a springboard into handling complex tasks in your day-to-day job, whether it be a professional, student, or hobbyist environment.
This course is a multi-part series ideal for those who are interested in understanding machine learning from a 101 perspective, and for those wanting to become data engineers. If you already understand concepts such as how to train and inference a model, you may wish to skip ahead to part two or a more advanced learning path.
It helps if you have a 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 will be able to more easily expand at your own pace with a good baseline understanding. As we explain the core concepts, there are some prerequisites for this course.
It is recommended that you have a basic familiarity with one of the cloud providers, especially AWS or GCP. Azure, Oracle, and other providers also have machine learning suites but these two are the focus for this class.
If you have an interest in completing the labs for hands on work, Python is a helpful language to understand.
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.
Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity. With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.