Amazon offers free machine learning courses

Anyone interested in machine learning, artificial intelligence, deep learning or any other variant in computer-based intelligence will know there’s a wealth of online materials out there that will teach the basics (and then some) about the tech to the utterly uninitiated through to the high-level developer.

But there are courses and materials on offer from the very organizations that you’ll probably end up using to host your cloud-based artificial intelligence (AI) routines once they’re up and running: Google, Microsoft, and Amazon, to name but three.

Latest to the party is Amazon’s AWS, now offering training courses previously only available to its paid-up cloud platform developer-users. There are around 30 courses online, with 45+ hours of course material and videos.

Amazon’s offering is designed to take users down various learning paths, depending on where they’re coming from, and what they want to achieve. Business owners will want to start with the course elements marked as ‘Fundamentals’, but there are modules too for expert programmers, data scientists and data platform engineers.

Users can gain an ‘AWS Certification for Machine Learning‘, after having coughed up the $150 exam fee (reduced from $300 at the moment). Any lab-based learning takes place on the AWS platform, of course, for which doesn’t come for free.

In a similar vein, Google’s AI courses offer videos, course notes, code examples and support from the existing user base of fellow students. Microsoft’s professional development ‘Tracks’ include an AI series, too– on that platform, there’s also data science and big data courses, alongside the more traditional network administration-type certifications.

Whichever the platform you decide upon, you’ll find that each of them offers specific sources for different audiences: for business decision-makers and owners, the courses will teach you how to use the technology in a commercial context. For developers, there are lessons in the specific Python calls and routines that are most effective, for example, plus there are step-throughs for common platforms, like the proprietary TensorFlow.

Marketing and customer behavior are a common starting point for many in business-oriented roles: click ‘journeys’, social media posts and historical shopping data can be combined, for instance, to test whether the next product will crash or soar. AI can help detect fraud and anomalous customer behavior – ML (machine learning) to a certain extent is all about pattern-recognition.

New #tutorial on #lex to make your own #bot! #DevOps #CloudComputing #AWS https://t.co/EwkwnOswvi @awscloud

— AWS Tutorial Series (@awstutseries) May 3, 2017

Image recognition is one of those areas in which AI is often quoted as being useful. But patterns can be determined in any data set, and once the algorithms begin to learn, they can be ‘corrected’ with real-life examples. Recognizing faces is one thing, recognizing suspect behavior can utilize the same type of technology.

As with any other business activity, you get out what you put into your learning. None of the cloud-based AI/ML providers suggest that results are achievable overnight. But there are courses specifically for data scientists, developers, database specialists and the like too, so those with some technical knowledge can get off to a flying start, and the rest of us can at least be aware of the issues, even if we’re not able to produce lean code ourselves.

Where there’s value to be gained by anyone is in the fact that although each series of courses is to certain extent platform-specific, there’s enough knowledge on offer that’s agnostic to be of significant value in any context– Natural Language Processing’s key elements are transferable from AWS to Google Cloud, for example.

Every emerging software product (and many hardware instances too) are using AI as a kind of shortcut to meaning ‘the very latest’ at the moment. For many in business, it’s probably a wise move to equip oneself with a little learning if it’s only to be able to discern the marketing hubris from reality.