The course is part of this learning path
Welcome to Part Two of an introduction to using Artificial Intelligence and Machine Learning. As we mentioned in part one, this course starts at the ground up and focuses on giving students the tools and materials they need to navigate the topic. 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.
In part one we looked at how you can use out-of-the-box machine learning models to meet your needs. In this course, we are going to build on that and look at how you can add your own functionality to these pre-canned models. We look at ML training concepts, release processes, and how ML services are used in a commercial setting. Finally, we take a look at a case study so that you get a feel for how these concepts play out in the real world.
For any feedback relating to this course, please contact us at firstname.lastname@example.org.
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; starting from a very basic level and ramping up over time. 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.
We have a hands on lab portion of this learning path, with a button shown here, in which you're able to start to train a model like we discussed. However, there is one little twist. Instead of focusing on Tudor or Victorian houses, we simply said, is to say single-family house or a skyscraper. This isn't as common of a real world scenario, but as you may imagine, it's a lot easier to train an initial model on because the pictures are so drastically different.
In this lab, you'll start with a hands-on SageMaker notebook using Amazon's Rekognition service, which is functionally Amazon's version of AutoML. You'll learn to create a classifier, which will differentiate between a single-family house and a skyscraper, and at the end of it, having a model, which you can reference for the duration of the lab, to predict your own images on.
So in conclusion, for those of you who have just completed the lab, or are just watching this lecture through and will soon do the lab, let's reiterate the key points from the first two modules. Hopefully, by this point, you'll have a good understanding of what it means to create leverage and inference artificial intelligence systems and machine learning applications from Amazon and Google, and hopefully you have a initial understanding of what it means to train a model within the predefined frameworks on the cloud providers and some basic commercial solutions.
These fundamentals are important for establishing a career in data science. And now that you understand them, we're able to move to more complex topics and really start to dive into how to create models. Keep your eye out for more classes on this learning path, and if you haven't done it yet, go check out the labs 'cause I believe they are extremely helpful for solidifying these lectures.
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.