Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Recent advances in algorithms, technology, and the availability of vast amounts of data allow machines to solve problems that were once considered out of reach. Machine learning is an exciting and rapidly growing field full of possibilities, but it can be intimidating at first.
If you want to quickly learn the essentials of machine learning without lots of math or code, then this course is for you. There's more to a successful ML project that just creating models and writing code. Identifying suitable problems, collecting, preparing and curating data sets, validating results, and maintaining quality over time are just as important as writing code. These challenges require a variety of skills, many of which are not super technical.
Whether you're a manager, business analyst, software developer, or someone looking to change careers, there's a place for you in a machine learning project. This course is aimed at giving you the knowledge you need to be productive in a changing economy where machines are climbing the corporate ladder.
There are a number of machine learning examples demonstrated throughout the course. Code examples are available on github. You have the option of hands-on experimentation with these examples on your local machine or Google Colab. Colab is a free, cloud-based machine learning and data science platform that includes GPU support to reduce model training time. Alternatively, some students are happy just watching the examples run and learning from the videos. It's completely up to you.
This is an introductory, thought based course. The course covers concepts that many might not have been exposed to before. Some of it might seem confusing in places, but that’s completely normal. Machine learning is quite different from conventional, imperative software. By the end of this course you will understand the benefits of machine learning, how it works, and what you need to do next.
July 2019 course updates include lectures and examples of self-supervised learning. Self-supervised learning is an exciting technique where machines learn from data without the need for expensive human labels. It works by predicting what happens next or what's missing in a data set. Self-supervised learning is partly inspired by early childhood learning and yields impressive results. You will have an opportunity to experiment with self-supervised learning to fully understand how it works and the problems it can solve.
August 2019 course updates include a step by step demo of how to load data into Google Colab using two different methods. Google Colab is a powerful machine learning environment with free GPU support. You can load your own data into Colab for training and testing.
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