The course is part of these learning paths
This course explores the core concepts of machine learning, the models available, and how to train them. We’ll take a deeper look at what it means to train a machine learning model, as well as the data and methods required to do so. We’ll also provide an overview of the most common models you’re likely to encounter, and take a practical approach to understand when and how to use them to solve business problems.
In the second half of this course, you will be guided through a series of case studies that will show you how to apply the concepts covered in this course to real-life examples.
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- Understand the key concepts and models related to machine learning
- Learn how to use training data sets with machine learning models
- Learn how to choose the best machine learning model to suit your requirements
- Understand how machine learning concepts can be applied to real-world scenarios in property prices, health, animal classification, and marketing activites
This course is intended for anyone who is:
- Interested in understanding machine learning models on a deeper level
- Looking to enrich their understanding of machine learning and how to use it to solve complex problems
- Looking to build a foundation for continued learning in the machine learning space and data science in general
To get the most out of this course, you should have a general understanding of data concepts as well as some familiarity with cloud providers and their managed services, especially Amazon or Google. Some experience in data or development is preferable but not essential.
Hey, everyone. Welcome to an introduction to machine learning in which we'll be discussing common models and how they apply.
Over this course, we really hope to give a hands-on practical understanding of some of the most common models that Amazon SageMaker platform supports. And although I say Amazon SageMaker platform, the models and the approaches here are really applicable to everything. But Amazon in particular has a lot of models pre-built into libraries that are readily assessable.
So over this class, we'll be showing you how to use these models, how to pick the right models, and hopefully by the end of it, you'll have a good understanding of at least how basic models work and how to pick the right one because of course in a one-hour class, we can't discuss every machine learning model ever.
So ideally, you'd have some background in data engineering or data science before jumping into this class. We're going to discuss some topics that assume you know how machine learning data is prepped, how and what a model is, and the relationships between models and training.
Ideally, as well, if you have a good understanding of SQL or at least common data structures, that would also be fantastic. That being said, if you just work with data scientists and you wanna understand more about the types of problems they can solve, this class is gonna be a bit in depth, more than just an overview, but if you wanna understand, we do keep it high level at times with lots of examples for you to follow along.
So the ideal person for this class is both anybody looking to get a deeper understanding into specific machine learning models or people who have to work with data scientists and data engineers and wanna gain a better understanding of their peers and colleagues' work.
A little bit about me before we get started. My name is Chris Gambino. I'm one of the founding architects at Calculated Systems. Before coming over here, I worked at both Google and Hortonworks working on big data problems. So this type of work is in my blood and has been for years.
Relevant to machine learning problems, we recently worked on some cool stuff around detecting coronavirus changes in the environment and modeling where outbreaks could occur simply through mass movements of people and using population statistics. So I can speak from that experience that notebooks are ideal for developing and refining your models and using endpoints is really important both from the decoupling nature of you have your machine learning and your execution, and from the ability to quickly iterate through new development.
Explaining Concepts - Models - Understanding Training Data Sets - How to Choose? - Case Study: Home Prices - Case Study: Heart Disease - Case Study: Animal Classification - Case Study: Targeted Marketing
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.